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REVIEW 4 major objections 7 minor 124 references

Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work

T0 review · 4 major / 7 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read A 7-day field study with 16 active gig workers from three platforms suggests that a purpose-built data-sharing app can be woven into daily work routines and that workers will share their data with policymakers and advocates.

desk verdict A solid first deployment of a cross-platform gig-worker data-sharing tool, with believable qualitative findings; the abstract overstates the sample count and the paper misses the chance to analyze the one attrition that matters. read the letter →

arxiv 2502.04482 v1 pith:2FYFB3PO submitted 2025-02-06 cs.HC

Jane Hsieh , Angie Zhang , Sajel Surati , Sijia Xie , Yeshua Ayala , Nithila Sathiya , Tzu-Sheng Kuo , Min Kyung Lee
show 1 more author
Haiyi Zhu
This is my paper · ORCID
classification cs.HC
keywords gigworkdata-sharingworkercollectivespolicymakingfieldstudymutualaidalgorithmicmanagementworker-centereddesign
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces Gig2Gether, a web prototype that lets gig workers from Uber, Rover, and Upwork log income and expenses, view personal and collective trends, and post stories about work. In a 7-day field study with 16 active workers, the authors find that data-sharing tools fit into daily workflows: workers exchange strategies and reassurance across platforms, and use tracked finances to reflect and plan. Workers also said they would share both numbers and personal accounts with policymakers and advocates, especially about safety and pay. The paper argues that such tools can complement unions and advocacy groups rather than replace them, and that cross-platform data sharing can help demystify how platform algorithms affect work.

What carries the argument

The carrying object is Gig2Gether itself, a responsive web app whose design requirements include supporting both quantitative and qualitative data sharing, building trust through privacy and data control, accommodating heterogeneous workflows, and connecting workers to other stakeholders. Its four linked mechanisms are: story sharing with tags and audience control, income and expense uploads (manual or CSV), personal and collective trend views, and a work planner. The story feed is where cross-platform mutual aid and policy-facing narratives happen; the trend pages turn uploaded records into individual and aggregate statistics. Together these turn data contribution from a one-off act of activism into a daily work practice.

What would settle it

A replication that pays no daily fee and observes a month of voluntary use would settle the claim; if uploads and story shares fall to near zero once the daily payment stops, the reported affordances are artifacts of compensation rather than durable work practices.

Watch

Extended reading notes

Core claim

The central discovery is that gig workers from different platforms will, in their everyday work, contribute qualitative and quantitative data to a shared tool when the tool serves their own needs first. In the study, participants authored 27 stories, liked stories from other platforms almost as often as from their own, and all logged income; many logged expenses. When asked, they said they would let policymakers see their data in 23 of 27 story postings, and they named safety and fair pay as the topics they most want officials to understand. The paper reads this as evidence that a worker-centered data-sharing platform can both support individual financial management and lay groundwork for collective action and evidence-driven regulation.

Load-bearing premise

The findings rest on the assumption that 7 days of compensated, minimum-task usage by a small, self-selected group of workers reflects how gig workers would voluntarily engage with a data-sharing tool in sustained real-world use.

Editorial extensions

If this is right

  • If the field study reflects real behavior, a cross-platform data-sharing tool can be woven into gig workers' daily routines without adding significant burden; several participants described uploads as becoming a natural part of their housekeeping.
  • Workers will use such a tool to support one another across platforms, such as a Rover sitter who liked an Upwork story because she was considering joining that platform.
  • Workers are willing to share both aggregate statistics and personal stories with policymakers and advocates, which could provide evidence for safety and wage regulation beyond what platforms disclose.
  • Even a Collective Insights page populated with mock data sparked worker questions about algorithmic effects, suggesting that aggregated data can power collective sensemaking about platform mechanics.
  • Data-sharing tools are best seen as complements to, not substitutes for, union organizing and informal mutual aid networks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If voluntary willingness to share survives the absence of daily payment, a tool like Gig2Gether could become a grassroots evidence base for safety regulations, filling the data void left by proprietary platform records.
  • A participant's suggestion to attach trip data to a story points to a design direction worth testing: coupling qualitative claims with quantitative evidence to increase credibility with policymakers.
  • The small freelancer sample leaves open whether remote freelancers would sustain engagement; a longer deployment with more Upwork users would test whether the tool's appeal generalizes across work domains.
  • Because the study paid workers per day, a natural next experiment is to compare engagement under no payment and under payment; the paper's claims would be stronger if usage persists unpaid.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. This paper presents Gig2Gether, a cross-platform data-sharing prototype for gig workers, and reports on a seven-day field study with gig workers recruited from Uber, Rover, and Upwork. The prototype was iteratively designed from prior design requirements and pilot think-aloud sessions with six workers; the field deployment collected usage logs plus onboarding and exit interviews. The authors report that workers used story sharing for cross-platform mutual support and income/expense logging for financial reflection, envisioned collective-insights and planning features as tools for algorithm speculation, rate setting, and policy advocacy, and expressed willingness to share data with policymakers and advocates. The paper also surfaces design implications around automatic data entry, net earnings and downtime metrics, anonymity, and moderation. According to Table 1, 16 workers were recruited and 14 completed the study, with two dropouts after onboarding; the manuscript inconsistently refers to "16 active workers" in the abstract and elsewhere.

Significance. If the reported claims are calibrated to the actual sample, this is a useful early empirical contribution to the growing literature on worker-centered data collectives. The paper's strengths are concrete: the authors built and deployed a working prototype, collected backend usage logs, analyzed qualitative interviews with real gig workers, and report several design tensions (e.g., anonymity vs. verifiability, mutual aid vs. forum toxicity) that are grounded in participant quotes. The iterative design process with pilot workers and the explicit positionality statement are also commendable. The evidence is strongest for driver and petsitter interactions and for financial reflection; the policy-impact and cross-platform claims are more speculative and need to be scoped accordingly.

major comments (4)
  1. [Abstract, §7.1, Table 1, §8.1.2] The paper's headline sample count is internally inconsistent. The abstract states "7-day field study with 16 active workers," and §8.1.2 says "9 of 16 uploaded expense entries," but Table 1 footnotes record that Driver-4 and Driver-5 dropped out after onboarding, leaving 14 completers. The usage totals in Table 3 (27 stories, 120 income uploads, 42 likes) match a 14-person denominator, not 16. This is not a purely cosmetic typo: Driver-4's stated reason—concern that participation would violate Uber policies—is direct negative evidence bearing on the feasibility claim and on design requirement DR1.2, and excluding him biases the sample toward workers who do not perceive such a conflict. Please correct all participant counts (e.g., "16 recruited, 14 completed") and add a discussion of how the attrition, especially the platform-retaliation-related dropout, qualifies the claims of existing affordances and willingness to share data.
  2. [§8.1.1, Table 2, §9.4] The cross-platform mutual-support claim is overgeneralized relative to the freelancer subsample. Only two Upwork freelancers completed the study; they authored one story in total, and the single freelancer "like" was directed at a driver story. The pattern of liking stories across domains is therefore demonstrated mainly for driver–petsitter pairs, not for all three platforms. Since the abstract's central claim asserts "three distinct platforms and work domains," please either rephrase the cross-platform finding as primarily driver–petsitter with limited freelancer input or provide additional evidence from the freelancer subsample.
  3. [§8.1.2, §6.4, Appendix A.3] Part of the central claim about "existing affordances of data-sharing—facilitating mutual support across platforms, as well as enabling financial reflection and planning" rests on features populated with mock data. The Planner was a prototype with simulated projections, and the Collective Insights page used mock data rather than participant data. The study therefore did not observe an existing planning affordance; it observed reactions to a simulated one. Please relabel the planning and collective-insights results as envisioned or anticipated use cases and adjust the abstract's "existing affordances" wording accordingly.
  4. [§7.2, Table 3] The usage statistics were produced under an explicit minimum-task requirement with daily payment ($15/day for one upload or story, up to $200 total). Consequently, the finding that data-sharing integrated into workers' daily workflows is at least partly a compliance effect; the logs cannot distinguish willing use from obligated use. Please either qualify the integration claim (e.g., "under an incentivized minimum-usage protocol") or present evidence about voluntary use, such as participants' own reminders, expressions of continued-use intention, or behavior beyond the minimum task, and discuss how the compensation may have affected the dropout decision.
minor comments (7)
  1. [§7.1, Table 1] The recruitment paragraph says "8 Uber drivers, 5 Rover petsitters, and 2 Upwork freelancers" (summing to 15), but Table 1 lists nine driver rows; please reconcile the numbers.
  2. [§8.2.3] The text quotes "Driver-10" when describing a desire to use the app to record subminimal wages, but Table 1 contains no Driver-10; please correct the participant identifier or add the missing row.
  3. [§8.2.1, Table 2] The text states that 23 of 27 stories were shared with policymakers, but Table 2's sharing categories that include policymakers sum to 24 (Policymakers Only 1, Workers + Policymakers 1, Workers + Policymakers + Advocates 22); please clarify the counting.
  4. [Table 2, Table 4] Table 4's "Total Liked" column sums to 64, while Table 2 reports 42 total likes; if Table 4 counts tag instances across liked stories rather than the number of likes, this should be stated in the caption or a footnote.
  5. [§8.1.2] The phrase "D3 (who did not previously use tracking tools)" should read "Driver-3."
  6. [References] References 19/20, 33/34, and 96/97 are duplicated entries; please consolidate them.
  7. [§7.3, Appendix A.2.1] The exit interview protocol asks specifically about each designed feature, so the salience of themes like mutual support and financial reflection may be shaped by the instrument itself; a sentence acknowledging this in the limitations would help.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims rest on empirical field-study observations, not on a derivation that reduces to its own inputs.

full rationale

Gig2Gether is an empirical HCI field study, not a formal derivation. The paper's claimed chain is: design requirements derived from prior literature (Section 4) -> prototype construction (Section 6) -> field evaluation with workers (Section 7) -> findings (Section 8). The central claims, such as 'existing affordances of data-sharing: facilitating mutual support across platforms, as well as enabling financial reflection and planning,' are supported by observed usage metrics (27 stories, 120 income uploads, 20 expense uploads in Table 3) and by participants' own interview statements, not by the design requirements themselves. The requirements were inputs to the system design, but the findings are reported observations about how participants actually used the system. No equation or fitted parameter is renamed as a prediction. The self-citations to the authors' prior work (e.g., [45], [120]) are used to justify design requirements, but the load-bearing evidence for the paper's contribution is the new field study, which is externally observable and not implied by those citations. The paper does not invoke a uniqueness theorem, does not define its outcome in terms of its input, and does not present a fitted value as a predicted result. The abstract's '16 active workers' versus Table 1's two dropouts is an internal-consistency and validity concern, not a circularity concern: it affects the strength of the empirical claim but does not mean the claim is true by construction. Accordingly, the appropriate circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central claims rest on study design choices (duration, compensation, daily task minimums) and on domain assumptions about self-report reliability, platform representativeness, qualitative coding, and sampling. The only 'invented entity' is the prototype itself, which is a deployed artifact.

free parameters (5)
  • Study duration = 7 days
    Chosen by the researchers; affects how much engagement and habit formation can be observed.
  • Daily task reward = $15/day plus $30 onboarding, $30 exit, $35 bonus up to $200
    Compensation chosen to incentivize participation; may inflate engagement relative to voluntary use.
  • Daily task requirement = One income/expense upload or one story per day
    Sets a minimum contribution level, so observed usage does not reflect purely voluntary behavior.
  • Participant count = 16 recruited, 14 completed
    Small, self-selected sample; limits generalizability, especially for Upwork (n=2).
  • Mock data population = Collective Insights and Planner populated with mock data
    Participants reacted to hypothetical aggregated statistics, so future-use-case findings are speculative.
assumptions (4)
  • domain assumption Participants' self-reported interview responses and logged usage accurately reflect their genuine work practices and attitudes.
    All findings are based on self-report and voluntary logging over 7 days; there is no independent verification of platform data.
  • domain assumption The three selected platforms (Uber, Rover, Upwork) represent distinct and comparable gig work domains.
    Used to justify cross-platform solidarity claims; actual imbalance (8 drivers, 5 petsitters, 2 freelancers) weakens this.
  • domain assumption Open coding by three researchers without reported inter-rater reliability yields reliable themes.
    Qualitative analysis methods; no intercoder agreement metrics reported in Section 7.4.
  • domain assumption Workers recruited through Reddit, Nextdoor, Craigslist, prior-study contacts, and airport flyers are representative of active gig workers.
    Sampling may over-represent workers already engaged in online communities and willing to accept $200 compensation.
invented entities (1)
  • Gig2Gether prototype independent evidence
    purpose: A web application for cross-platform gig worker data upload, story sharing, trend visualization, planning, and tax resources.
    The system was actually built and deployed for 7 days with 14 workers; it is a concrete artifact, not a theoretical postulate.

how reviews work

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Cite this review

Pith. "Pith review of Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work." pith.science (2026). https://pith.science/paper/2FYFB3PO

@misc{pith2026250204482,
  author       = {Pith},
  title        = {Pith review of: Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2FYFB3PO}},
  note         = {Machine review of arXiv:2502.04482}
}
read the original abstract

The wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are exposed to unprecedented challenges and accountabilities: lack of data transparency, social and physical isolation, as well as insufficient infrastructural safeguards. Gig2Gether presents a space designed for workers to engage in an initial experience of voluntarily contributing anecdotal and statistical data to affect policy and build solidarity across platforms by exchanging unifying and diverse experiences. Our 7-day field study with 16 active workers from three distinct platforms and work domains showed existing affordances of data-sharing: facilitating mutual support across platforms, as well as enabling financial reflection and planning. Additionally, workers envisioned future use cases of data-sharing for collectivism (e.g., collaborative examinations of algorithmic speculations) and informing policy (e.g., around safety and pay), which motivated (latent) worker desiderata of additional capabilities and data metrics. Based on these findings, we discuss remaining challenges to address and how data-sharing tools can complement existing structures to maximize worker empowerment and policy impact.

Figures

Figures reproduced from arXiv: 2502.04482 by the authors.

Figure 1
Figure 1. Gig2Gether features engage workers across platforms in qualitative data exchange via the Story Sharing and Feed (a), [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Initial Sketches – (a) aligns to DR1.3 & DR2.3 , (b) accommodates DR1.2 & DR2.2 , (c) targets DR2.1 & DR2.3 key features, guided by above design requirements. To accommo￾date diverse worker goals and workflows DR1.3 , and varying work domains DR2.3 , we designed multiple data upload methods (see (a) of [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Web Versions of Mid-fidelity Wireframes When sharing stories, workers are required to choose (1) related tag(s) — prepopulated with themes identified from Section 4 — (2) whether their Story represents an issue or strategy, and (3) who to share the Story to. The Story feature — [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Overview of Gig2Gether Features for Before, After and Between Gigs [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 6
Figure 6. Figure 6: Experienced workers also shared strategies for improving [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 5
Figure 5. Figure 5: Petsitter-1’s response to another strategy [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 8
Figure 8. Figure 8: Petsitter-2’s Fear of Cancellations [PITH_FULL_IMAGE:figures/full_fig_p023_8.png]
Figure 6
Figure 6. Figure 6: Driver-1’s Strategy: Self-Protecting from a Trespass [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]
Figure 7
Figure 7. Figure 7: Driver-2’s Strategy: More Reservation Assignments [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]
Figure 11
Figure 11. Figure 11: Driver-3 Frustration on Reservation Assignments [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]
Figure 14
Figure 14. Figure 14: Driver-7’s Observations on Unpaid Time [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]
Figure 12
Figure 12. Figure 12: Driver-1’s strategy: Small Claims Lawsuits [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]
Figure 16
Figure 16. Figure 16: Petsitter-4’s Strategy to Record Unpaid Tasks [PITH_FULL_IMAGE:figures/full_fig_p025_16.png]

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Reference graph

Works this paper leans on

124 extracted references · 52 canonical work pages

  1. [1]

    https://leg.colorado.gov/sites/default/files/2024a_1129_signed.pdf

    2024. https://leg.colorado.gov/sites/default/files/2024a_1129_signed.pdf

    2024

  2. [2]

    Beatrice Oyinkansola Adelakun. 2023. Tax compliance in the gig economy: the need for transparency and accountability. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1, 1 (2023), 191–198. https: //doi.org/10.60087/jklst.vol1.n1.p219

    work page doi:10.60087/jklst.vol1.n1.p219 2023

  3. [3]

    Moritz Altenried. 2019. On the last mile: logistical urbanism and the trans- formation of labour. Work Organisation, Labour & Globalisation 13, 1 (2019), 114–129

    2019

  4. [4]

    Bénédicte Apouey, Alexandra Roulet, Isabelle Solal, and Mark Stabile. 2020. Gig workers during the COVID-19 crisis in France: financial precarity and mental well-being. Journal of urban health 97, 6 (2020), 776–795

    2020

  5. [5]

    Uttam Bajwa, Denise Gastaldo, Erica Di Ruggiero, and Lilian Knorr. 2018. The health of workers in the global gig economy. Globalization and health 14 (2018), 1–4

    2018

  6. [6]

    Emma Bartel, Ellen MacEachen, Emily Reid-Musson, Samantha B Meyer, Ron Saunders, Philip Bigelow, Agnieszka Kosny, and Sharanya Varatharajan. 2019. Stressful by design: Exploring health risks of ride-share work. Journal of Trans- port & Health 14 (2019), 100571

    2019

  7. [7]

    Thor Berger, Carl Benedikt Frey, Guy Levin, and Santosh Rao Danda. 2019. Uber happy? Work and well-being in the ‘gig economy’.Economic Policy 34, 99 (2019), 429–477. https://doi.org/10.1093/epolic/eiz007

    work page doi:10.1093/epolic/eiz007 2019

  8. [8]

    Allie Blaising, Yasmine Kotturi, Chinmay Kulkarni, and Laura Dabbish. 2021. Making it Work, or Not: A Longitudinal Study of Career Trajectories Among Online Freelancers. Proc. ACM Hum.-Comput. Interact. 4, CSCW3, Article 226 (jan 2021), 29 pages. https://doi.org/10.1145/3432925

    work page doi:10.1145/3432925 2021

Show all 124 references
  1. [9]

    Alan Borning and Michael Muller. 2012. Next steps for value sensitive design. In Proceedings of the SIGCHI conference on human factors in computing systems . 1125–1134

    2012

  2. [10]

    Eliane Léontine Bucher, Peter Kalum Schou, and Matthias Waldkirch. 2021. Pacifying the algorithm–Anticipatory compliance in the face of algorithmic management in the gig economy. Organization 28, 1 (2021), 44–67

    2021

  3. [11]

    Damion Jonathan Bunders. 2021. Gigs of their own: Reinventing worker co- operativism in the platform economy and its implications for collective ac- tion. In Platform economy puzzles. Edward Elgar Publishing, 188–208. https: //doi.org/10.4337/9781839100284.00019

    2021

  4. [12]

    Dan Calacci. 2022. Organizing in the end of employment: information sharing, data stewardship, and digital workerism. InProceedings of the 1st Annual Meeting of the Symposium on Human-Computer Interaction for Work . 1–9

    2022

  5. [13]

    Dan Calacci and Alex Pentland. 2022. Bargaining with the Black-Box: Designing and Deploying Worker-Centric Tools to Audit Algorithmic Management. Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Article 428 (nov 2022), 24 pages. https: //doi.org/10.1145/3570601

    2022 doi

  6. [14]

    Dan Calacci and Jake Stein. 2023. From access to understanding: Collective data governance for workers. European Labour Law Journal 14, 2 (2023), 253–282. https://doi.org/10.1177/20319525231167981

    2023 doi

  7. [15]

    Lindsey D Cameron and Hatim Rahman. 2022. Expanding the locus of resistance: Understanding the co-constitution of control and resistance in the gig economy. Organization Science 33, 1 (2022), 38–58. https://doi.org/10.1287/orsc.2021.1557

    2022

  8. [16]

    Cecchinato, and John Rooksby

    Juan Carlos Alvarez de la Vega, Marta E. Cecchinato, and John Rooksby

  9. [17]

    Brianna B Caza, Erin M Reid, Susan J Ashford, and Steve Granger. 2022. Working on my own: Measuring the challenges of gig work. Human Relations 75, 11 (2022), 2122–2159

    2022

  10. [18]

    Becoming an expert in driving for Uber

    Ngai Keung Chan. 2019. “Becoming an expert in driving for Uber”: Uber driver/bloggers’ performance of expertise and self-presentation on YouTube. New media & society 21, 9 (2019), 2048–2067. https://doi.org/10.1177/ 1461444819837736

    2019

  11. [20]

    Nicola Christie and Heather Ward. 2019. The health and safety risks for people who drive for work in the gig economy. Journal of Transport & Health 13 (2019), 115–127. https://doi.org/10.1016/j.jth.2019.02.007

    2019 doi

  12. [21]

    Vaughn Cockayne. 2023. NLRB decision makes it easier for gig workers to Unionize. The Washington Times (Jun 2023). https://www.washingtontimes. com/news/2023/jun/14/nlrb-decision-makes-it-easier-gig-workers-unionize/

    2023

  13. [22]

    Union Coded. 2023. https://unioncoded.com/how-labor-union-dues- are-utilized-a-transparent-look-into-funding-and-impact/#:~:text=Labor% 20union%20dues%20form%20a,their%20rights%20in%20the%20workplace

    2023

  14. [23]

    Matthew Cole, Mark Stuart, Kate Hardy, and David Spencer. 2024. Wage theft and the struggle over the working day in hospitality work: a typology of unpaid labour time. Work, Employment and Society 38, 1 (2024), 103–121

    2024

  15. [24]

    Ruth Berins Collier, Veena Dubal, and Christopher Carter. 2017. Labor platforms and gig work: the failure to regulate. Legal Studies Research Paper Series (2017). https://doi.org/10.2139/ssrn.3039742

    2017 doi

  16. [25]

    Brett Collins, Andrew Garin, Emilie Jackson, Dmitri Koustas, and Mark Payne

  17. [26]

    Lincoln Dahlberg. 2001. Computer-mediated communication and the public sphere: A critical analysis. Journal of Computer-mediated communication 7, 1 (2001), JCMC714

    2001

  18. [27]

    Samantha Dalal, Ngan Chiem, Nikoo Karbassi, Yuhan Liu, and Andrés Monroy- Hernández. 2023. Understanding Human Intervention in the Platform Economy: A case study of an indie food delivery service. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems ...

    2023

  19. [28]

    Xuefei Nancy Deng and Robert D Galliers. 2024. Toward an Understanding of Gig Work Risks and Worker Agency on Different Digital Labor Platforms. Journal of the Association for Information Systems 25, 5 (2024), 1163–1193

    2024

  20. [29]

    Kimberly Do, Maya De Los Santos, Michael Muller, and Saiph Savage. 2024. Designing Gig Worker Sousveillance Tools. InProceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24). Asso- ciation for Computing Machinery, New York, NY, USA,...

    2024

  21. [30]

    Veena Dubal. 2023. On algorithmic wage discrimination. Columbia Law Review 123, 7 (2023), 1929–1992. https://doi.org/stable/27264954

    2023

  22. [31]

    Gaëlle Ferrant, Luca Maria Pesando, and Keiko Nowacka. 2014. Unpaid Care Work: The missing link in the analysis of gender gaps in labour outcomes. Boulogne Billancourt: OECD Development Center 20 (2014)

    2014

  23. [32]

    Paul Glavin, Alex Bierman, and Scott Schieman. 2021. Über-alienated: Powerless and alone in the gig economy. Work and Occupations 48, 4 (2021), 399–431. https://doi.org/10.1177/07308884211024711

    2021 doi

  24. [34]

    Mark Graham, Vili Lehdonvirta, Alex Wood, Helena Barnard, Isis Hjorth, and Peter D Simon. 2017. The risks and rewards of online gig work at the global margins. Oxford Internet Institute (2017)

    2017

  25. [35]

    Mary L Gray and Siddharth Suri. 2019. Ghost work: How to stop Silicon Valley from building a new global underclass . Eamon Dolan Books

    2019

  26. [36]

    Independent Worker

    Seth D Harris and Alan B Krueger. 2015. A Proposal for Modernizing Labor Laws for Twenty-First-Century Work: The" Independent Worker" . Brookings Washington, DC

    2015

  27. [37]

    Adrian John Hawley. 2018. Regulating labour platforms, the data deficit. Euro- pean Journal of Government and Economics 7, 1 (2018), 5–23

    2018

  28. [38]

    Troy Henderson and Tom Swann. 2017. Excessive hours, unpaid overtime and the future of work: an update. https://policycommons.net/artifacts/2039027/ excessive-hours-unpaid-overtime-and-the-future-of-work/2791470/

    2017

  29. [39]

    Rie Helene (Lindy) Hernandez, Qiurong Song, Yubo Kou, and Xinning Gui

  30. [40]

    Pamela Hopwood, Ellen MacEachen, Ivy Bourgeault, Carrie McAiney, Basak Yanar, and Abbey Davis. 2024. On-Demand and Marketplace Platforms: Gig Care Work Conditions on Two Digital Labour Platform Care Models. Critical Sociology (2024), 08969205241279868

    2024

  31. [41]

    Jane Hsieh, Oluwatobi Adisa, Sachi Bafna, and Haiyi Zhu. 2023. Designing Individualized Policy and Technology Interventions to Improve Gig Work Con- ditions. In Proceedings of the 2nd Annual Meeting of the Symposium on Human- Computer Interaction for Work. 1–9. https://doi.org...

    2023

  32. [43]

    Jane Hsieh, Miranda Karger, Lucas Zagal, and Haiyi Zhu. 2023. Co-Designing Alternatives for the Future of Gig Worker Well-Being: Navigating Multi- Stakeholder Incentives and Preferences. In Proceedings of the 2023 ACM De- signing Interactive Systems Conference (Pittsburgh, PA,...

    2023

  33. [44]

    Jane Hsieh, Angie Zhang, Seyun Kim, Varun Nagaraj Rao, Samantha Dalal, Alexandra Mateescu, Rafael Do Nascimento Grohmann, Motahhare Eslami, Min Kyung Lee, and Haiyi Zhu. 2024. Worker Data Collectives as a means to Improve Accountability, Combat Surveillance and Reduce Inequali...

    2024

  34. [45]

    Jane Hsieh, Angie Zhang, Mialy Rasetarinera, Erik Chou, Daniel Ngo, Jason Carpenter, Karen Lightman, Min Kyung Lee, and Haiyi Zhu. 2024. Supporting Gig Worker Needs and Advancing Policy Through Worker-Centered Data- Sharing. (2024). https://doi.org/10.48550/arXiv.2412.02973

    2024 doi arXiv

  35. [46]

    Kashif Imteyaz, Claudia Flores-Saviaga, and Saiph Savage. 2024. GigSense: An LLM-Infused Tool for Workers’ Collective Intelligence. arXiv preprint arXiv:2405.02528 (2024)

    2024 arXiv

  36. [47]

    Irani and M

    Lilly C. Irani and M. Six Silberman. 2013. Turkopticon: interrupting worker invisibility in amazon mechanical turk. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Paris, France) (CHI ’13). Association for Computing Machinery, New York, NY, USA, ...

    2013

  37. [48]

    Ken Jacobs and Michael Reich. 2021. Massachusetts Uber/Lyft Ballot Proposition Would Create Subminimum Wage: Drivers Could Earn as Little as $4.82 an Hour. Institute for Research on Labor and Employment (2021)

    2021

  38. [49]

    Saru Jayaraman. 2021. One fair wage: Ending subminimum pay in America . The New Press

    2021

  39. [50]

    Hannah Johnston, Chris Land-Kazlauskas, et al. 2018. Organizing on-demand: Representation, voice, and collective bargaining in the gig economy. Conditions of Work and Employment Series No. 94 (2018)

    2018

  40. [51]

    James Richards Katie Myhill and Kate Sang. 2021. Job quality, fair work and gig work: the lived experience of gig workers. The International Journal of Human Resource Management 32, 19 (2021), 4110–4135. https://doi.org/10.1080/ 09585192.2020.1867612

    2021

  41. [52]

    Maria Hameed Khan, Penny Williams, Robyn Mayes, Paula McDonald, and Jan- nine Williams. 2024. Care work in the gig economy.QUT Centre for Justice (2024). Issue 50. https://research.qut.edu.au/centre-for-justice/wp-content/uploads/ sites/304/2024/05/BP50_KhanWilliamsMayesMcDona...

    2024

  42. [53]

    Vera Khovanskaya, Phoebe Sengers, and Lynn Dombrowski. 2020. Bottom- Up organizing with tools from on high: Understanding the data practices of labor organizers. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13

    2020

  43. [54]

    Gesa Kirsch and Gesa E Kirsch. 1999. Ethical dilemmas in feminist research: The politics of location, interpretation, and publication . Suny press

    1999

  44. [55]

    Marion Koelle, Swamy Ananthanarayan, and Susanne Boll. 2020. Social Ac- ceptability in HCI: A Survey of Methods, Measures, and Design Strategies. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Comp...

    2020

  45. [56]

    Kristine M Kuhn and Tera L Galloway. 2019. Expanding perspectives on gig work and gig workers. Journal of Managerial Psychology 34, 4 (2019), 186–191. https://doi.org/10.1108/JMP-05-2019-507

    2019 doi

  46. [57]

    Tzu-Sheng Kuo, Quan Ze Chen, Amy X Zhang, Jane Hsieh, Haiyi Zhu, and Kenneth Holstein. 2024. PolicyCraft: Supporting Collaborative and Partici- patory Policy Design through Case-Grounded Deliberation. arXiv preprint arXiv:2409.15644 (2024)

    2024 arXiv

  47. [59]

    Vili Lehdonvirta. 2018. Flexibility in the gig economy: managing time on three online piecework platforms. New Technology, Work and Employment 33, 1 (2018), 13–29

    2018

  48. [60]

    Eric Leverage and Samantha Dalal. 2022. THE GIG GAP THE REALITY OF DENVER GIG WORKERS 2022 REPORT. http://www.cojwj.org/uploads/2/4/6/ 1/24613827/cjwj_white_paper_rd4.pdf

    2022

  49. [61]

    Chia Yu Lien. 2023. Temp nurses go digital: Examining gig care in US nursing homes. Sociology of health & illness 45, 3 (2023), 542–559

    2023

  50. [62]

    Isabell Lippert, Kathrin Kirchner, and Martin Wiener. 2023. Context Matters: The Use of Algorithmic Management Mechanisms in Platform, Hybrid, and Traditional Work Contexts. In Proceedings of the 56th Hawaii International Conference on System Sciences . 5282–5291

    2023

  51. [63]

    Brush it Off

    Ning F. Ma, Veronica A. Rivera, Zheng Yao, and Dongwook Yoon. 2022. “Brush it Off”: How Women Workers Manage and Cope with Bias and Harassment in Gender-agnostic Gig Platforms. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, US...

    2022

  52. [64]

    Robert MacDonald and Andreas Giazitzoglu. 2019. Youth, enterprise and precar- ity: or, what is, and what is wrong with, the ‘gig economy’?Journal of Sociology 55, 4 (2019), 724–740. https://doi.org/10.1177/1440783319837604

    2019 doi

  53. [65]

    Michael David Maffie. 2020. Are we ‘sharing’or ‘gig-ing’? A classification system for online platforms. Industrial Relations Journal 51, 6 (2020), 536–555

    2020

  54. [66]

    Eliza McCullough and Brian Dolber. 2021. Most California Rideshare drivers are not receiving health-care benefits under Proposition 22: National equity atlas. https://nationalequityatlas.org/prop22

    2021

  55. [67]

    Eliza McCullough, Brian Dolber, Justin Scoggins, Edward-Michael Muña, and Sarah Treuhaft. 2022. Prop 22 depresses wages and deepens inequities for California Workers: National Equity Atlas. https://nationalequityatlas.org/ prop22-paystudy

    2022

  56. [68]

    Joy Ming. 2023. Data Advocacy for Visibility of Home Care Workers. , 4 pages. https://doi.org/10.1145/3584931.3608922

    2023

  57. [69]

    Joy Ming, Dana Gong, Chit Sum Eunice Ngai, Madeline Sterling, Aditya Vashistha, and Nicola Dell. 2024. Wage Theft and Technology in the Home Care Context. Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024), 1–30. https://doi.org/10.1145/3637428

    2024 doi

  58. [70]

    I go beyond and beyond

    Joy Ming, Elizabeth Kuo, Katie Go, Emily Tseng, John Kallas, Aditya Vashistha, Madeline Sterling, and Nicola Dell. 2023. " I go beyond and beyond" examining the invisible work of home health aides. Proceedings of the ACM on Human- Computer Interaction 7, CSCW1 (2023), 1–21

    2023

  59. [71]

    Mareike Möhlmannn, Carolina Alves de Lima Salge, and Marco Marabelli. 2023. Algorithm sensemaking: how platform workers make sense of algorithmic management. Journal of the Association for Information Systems 24, 1 (2023), 35–64

    2023

  60. [72]

    Sarah Mosseri. 2022. Being watched and being seen: Negotiating visibility in the NYC ride-hail circuit. New Media & Society 24, 3 (2022), 600–620. https: //doi.org/10.1177/1461444820966752

    2022 doi

  61. [73]

    New York City Department of Consumer and Worker Protection. 2024. Food Delivery Worker Laws - FAQs. https://www.nyc.gov/site/dca/workers/ workersrights/food-delivery-worker-laws-faqs.page Accessed: 2025-02-02

    2024

  62. [74]

    Duy Quy Nguyen-Phuoc, Nguyen An Ngoc Nguyen, Minh Hieu Nguyen, Ly Ngoc Thi Nguyen, and Oscar Oviedo-Trespalacios. 2022. Factors influencing road safety compliance among food delivery riders: An extension of the job demands-resources (JD-R) model. Transportation research part A...

    2022

  63. [75]

    Marie Nilsen and Trond Kongsvik. 2023. Health, safety, and well-being in platform-mediated work–a job demands and resources perspective. Safety science 163 (2023), 106130

    2023

  64. [76]

    NLRB. 2024. Union petitions filed with NLRB Double since FY 2021, up 27% since FY 2023. https://www.nlrb.gov/news-outreach/news-story/union-petitions- filed-with-nlrb-double-since-fy-2021-up-27-since-fy-2023

    2024

  65. [77]

    Shu-Yi Oei and Diane M Ring. 2017. The tax lives of Uber drivers: evidence from internet discussion forums. Colum. J. Tax L. 8 (2017), 56. https://doi.org/ 10.7916/cjtl.v8i2.2842

    2017 doi

  66. [78]

    James A Parrott and Michael Reich. 2018. An earnings standard for new york city’s app-based drivers

    2018

  67. [79]

    Fernanda Pires, José M Tomasena, and Martina Piña. 2024. Delivery riders’ cultural production in Spain: A thematic analysis of their self-representation on YouTube. Convergence 30, 1 (2024), 588–604. https://doi.org/10.1177/ 13548565231161252

    2024

  68. [80]

    Sophie Pychlau. 2023. Understanding Belongingness in the Gig Economy: The Uplifting and Undermining Effects of Online Communities on Lonely Gig Work- ers. Ph. D. Dissertation. University of Oregon. https://www.proquest. com/docview/2842733540?pq-origsite=gscholar&fromopenview=...

    2023

  69. [81]

    Rida Qadri. 2021. What’s in a network? Infrastructures of mutual aid for dig- ital platform workers during COVID-19. Proceedings of the ACM on Human- Computer Interaction 5, CSCW2 (2021), 1–20

    2021

  70. [82]

    Casey Quinlan. 2024. States are pushing back with anti-labor laws as union popularity grows, policy experts say • Missouri Indepen- dent. https://missouriindependent.com/2024/09/11/states-are-pushing-back- with-anti-labor-laws-as-union-popularity-grows-policy-experts-say/

    2024

  71. [83]

    Hatim A Rahman, Arvind Karunakaran, and Lindsey D Cameron. 2024. Taming platform power: Taking accountability into account in the management of platforms. Academy of Management Annals 18, 1 (2024), 251–294. https: CHI ’25, April 26-May 1, 2025, Yokohama, Japan Hsieh & Zhang et...

    2024

  72. [84]

    Michael Reich and James A Parrott. 2020. A Minimum Compensation Standard for Seattle TNC Drivers

    2020

  73. [85]

    Reuters. 2024. VW workers step up strikes to fight German plant clo- sures. https://www.reuters.com/business/autos-transportation/vw-workers- step-up-strikes-fight-german-plant-closures-2024-12-05/

    2024

  74. [86]

    Katie Robertson. 2024. New York Times Tech Guild Ends Strike. https://www.nytimes.com/2024/11/11/business/media/new-york-times- tech-guild-ends-strike.html

    2024

  75. [87]

    Hall, Josh Hursey, and Tammy Toscos

    Yvonne Rogers, Kay Connelly, Lenore Tedesco, William Hazlewood, Andrew Kurtz, Robert E. Hall, Josh Hursey, and Tammy Toscos. 2007. Why It’s Worth the Hassle: The Value of In-Situ Studies When Designing Ubicomp. InUbiComp 2007: Ubiquitous Computing, John Krumm, Gregory D. Abowd...

    2007

  76. [88]

    John Rooksby, Mattias Rost, Alistair Morrison, and Matthew Chalmers. 2014. Personal tracking as lived informatics. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI ’14). Association for Computing Machinery, New York,...

    2014

  77. [89]

    Alex Rosenblat and Luke Stark. 2016. Algorithmic labor and information asym- metries: A case study of Uber’s drivers. International journal of communication 10 (2016), 27

    2016

  78. [90]

    Kirstein Rummery and Michael Fine. 2012. Care: A critical review of theory, policy and practice. Social Policy & Administration 46, 3 (2012), 321–343

    2012

  79. [91]

    Irani, Michael S

    Niloufar Salehi, Lilly C. Irani, Michael S. Bernstein, Ali Alkhatib, Eva Ogbe, Kristy Milland, and Clickhappier. 2015. We Are Dynamo: Overcoming Stalling and Friction in Collective Action for Crowd Workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in C...

    2015

  80. [92]

    Shruti Sannon and Dan Cosley. 2022. Toward a More Inclusive Gig Economy: Risks and Opportunities for Workers with Disabilities.Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Article 335 (Nov. 2022), 31 pages. https://doi.org/10.1145/ 3555755

    2022

  81. [93]

    Shruti Sannon, Billie Sun, and Dan Cosley. 2022. Privacy, Surveillance, and Power in the Gig Economy. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Artic...

    2022

  82. [94]

    Juliet B Schor and William Attwood-Charles. 2017. The “sharing” economy: la- bor, inequality, and social connection on for-profit platforms.Sociology Compass 11, 8 (2017), e12493. https://doi.org/10.1111/soc4.12493

    2017 doi

  83. [95]

    Juliet B Schor, Christopher Tirrell, and Steven Peter Vallas. 2024. Consent and Contestation: How Platform Workers Reckon with the Risks of Gig Labor. Work, Employment and Society 38, 5 (2024), 1423–1444. https://doi.org/10.1177/ 09500170231199404 arXiv:https://doi.org/10.1177...

    2024 doi

  84. [96]

    Peter Kalum Schou and Eliane Bucher. 2023. Divided we fall: The breakdown of gig worker solidarity in online communities. New Technology, Work and Employment 38, 3 (2023), 472–492. https://doi.org/10.1111/ntwe.12260

    2023 doi

  85. [97]

    Peter Kalum Schou and Eliane Bucher. 2023. Divided we fall: The breakdown of gig worker solidarity in online communities. New Technology, Work and Employment 38, 3 (2023), 472–492

    2023

  86. [98]

    Bhavani Seetharaman, Joyojeet Pal, and Julie Hui. 2021. Delivery work and the experience of social isolation. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–17

    2021

  87. [99]

    Minkyu Shin, Jiwoong Shin, Soheil Ghili, and Jaehwan Kim. 2023. The impact of the gig economy on product quality through the labor market: Evidence from ridesharing and restaurant quality. Management Science 69, 5 (2023), 2620–2638. https://doi.org/10.1287/mnsc.2022.4481

    2023

  88. [100]

    Jake M L Stein, Vidminas Vizgirda, Max Van Kleek, Reuben Binns, Jun Zhao, Rui Zhao, Naman Goel, George Chalhoub, Wael S Albayaydh, and Nigel Shadbolt

  89. [101]

    Will Sutherland and Mohammad Hossein Jarrahi. 2017. The Gig Economy and Information Infrastructure: The Case of the Digital Nomad Community. Proc. ACM Hum.-Comput. Interact. 1, CSCW, Article 97 (dec 2017), 24 pages. https://doi.org/10.1145/3134732

    2017 doi

  90. [102]

    Zhi Ming Tan, Nikita Aggarwal, Josh Cowls, Jessica Morley, Mariarosaria Taddeo, and Luciano Floridi. 2021. The ethical debate about the gig economy: A review and critical analysis. Technology in Society 65 (2021), 101594. https://doi.org/10. 1016/j.techsoc.2021.101594

    2021

  91. [103]

    Kelvin Taylor, Pieter Van Dijk, Sharon Newnam, and Dianne Sheppard. 2023. Physical and psychological hazards in the gig economy system: A systematic review. Safety science 166 (2023), 106234

    2023

  92. [104]

    Kathleen DeLaney Thomas. 2018. Taxing the gig economy. University of Pennsylvania Law Review 166 (2018), 1415–1473. https://www.jstor.org/stable/ 45154965

    2018

  93. [105]

    Julia Ticona, Alexandra Mateescu, and Alex Rosenblat. 2018. Beyond disruption: How tech shapes labor across domestic work and ridehailing

    2018

  94. [106]

    Carlos Toxtli, Siddharth Suri, and Saiph Savage. 2021. Quantifying the invisible labor in crowd work. Proceedings of the ACM on human-computer interaction 5, CSCW2 (2021), 1–26

    2021

  95. [107]

    Yana van der Meulen Rodgers and Elaine Zundl. 2019. The future of work in New Jersey: care workers and the gig economy

    2019

  96. [108]

    Niels Van Doorn and Adam Badger. 2020. Platform capitalism’s hidden abode: producing data assets in the gig economy. Antipode 52, 5 (2020), 1475–1495

    2020

  97. [109]

    Nicholas Vincent, Hanlin Li, Nicole Tilly, Stevie Chancellor, and Brent Hecht

  98. [110]

    Tracey Warren. 2021. Work-life balance and gig work:‘Where are we now’and ‘where to next’with the work–life balance agenda?Journal of Industrial Relations 63, 4 (2021), 522–545. https://doi.org/10.1177/00221856211007161

    2021 doi

  99. [111]

    Working Washington. 2019. Delivering Inequality, What Instacart Really Pays, and How the Company Shifts Costs to Workers

    2019

  100. [112]

    Gwendolyn Paige Watson, Lauren D Kistler, Baylor A Graham, and Robert R Sin- clair. 2021. Looking at the gig picture: Defining gig work and explaining profile differences in gig workers’ job demands and resources. Group & Organization Management 46, 2 (2021), 327–361. https://...

    2021 doi

  101. [113]

    Alex Wood, Nicholas Martindale, and Brendan Burchell. 2023. Gig Rights & Gig Wrongs Initial Findings from the Gig Rights Project: Labour Rights, Co- Determination, Collectivism and Job Quality in the UK Gig Economy. https: //doi.org/10.2139/ssrn.4446226

    2023 doi

  102. [114]

    In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21)

    Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21). Association for Computing Machinery, New York, NY, U...

    2021

  103. [115]

    Alex J Wood, Mark Graham, Vili Lehdonvirta, and Isis Hjorth. 2019. Networked but commodified: The (dis) embeddedness of digital labour in the gig economy. Sociology 53, 5 (2019), 931–950. https://doi.org/10.1177/0038038519828906

    2019 doi

  104. [116]

    Jonathan Woodside, Tara Vinodrai, and Markus Moos. 2021. Bottom-up strate- gies, platform worker power and local action: Learning from ridehailing drivers. Local Economy 36, 4 (2021), 325–343. https://doi.org/10.1177/02690942211040170

    2021 doi

  105. [117]

    Rio Yamat. 2024. Hundreds of hotel workers go on strike at Virgin Hotels Las Vegas. https://www.pbs.org/newshour/nation/hundreds-of-hotel-workers-go- on-strike-at-virgin-hotels-las-vegas

    2024

  106. [118]

    Zheng Yao. 2024. Peer Support in Online Communities . Ph. D. Dissertation. Carnegie Mellon University. http://reports-archive.adm.cs.cmu.edu/anon/hcii/ CMU-HCII-24-107.pdf#page=81.17

    2024

  107. [119]

    Alex J Wood, Mark Graham, Vili Lehdonvirta, and Isis Hjorth. 2019. Good gig, bad gig: autonomy and algorithmic control in the global gig economy. Work, employment and society 33, 1 (2019), 56–75

    2019

  108. [120]

    Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, and Min Kyung Lee. 2023. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, German...

    2023

  109. [121]

    Angie Zhang, Alexander Boltz, Chun Wei Wang, and Min Kyung Lee. 2022. Algorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (...

    2022

  110. [122]

    Angie Zhang, Rocita Rana, Alexander Boltz, Veena Dubal, and Min Kyung Lee. 2024. Data Probes as Boundary Objects for Technology Policy Design: Demystifying Technology for Policymakers and Aligning Stakeholder Objectives in Rideshare Gig Work. In Proceedings of the 2024 CHI Con...

    2024

  111. [124]

    Zheng Yao, Silas Weden, Lea Emerlyn, Haiyi Zhu, and Robert E Kraut. 2021. Together but alone: Atomization and peer support among gig workers. Pro- ceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–29. https://doi.org/10.1145/3479535

    2021 doi

  112. [128]

    My Trends

    be associated with at least one tag, 3) contain story content via a title or textual description, and 4) have a selection of desired viewing audience – this can include other worker users of the system, policymakers, advocates, or be entirely private (i.e. visible only to them...

    2025

  113. [2019]

    Unpublished paper, IRS SOI Joint Statistical Research Program (2019)

    Is gig work replacing traditional employment? Evidence from two decades of tax returns. Unpublished paper, IRS SOI Joint Statistical Research Program (2019)

    2019

  114. [2021]

    Why lose control?

    “Why lose control?” A Study of Freelancers’ Experiences with Gig Economy Platforms. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 455, 14 pages. http...

    2021

  115. [2023]

    There’s nobody else

    ‘You are you and the app. There’s nobody else. ’: Building Worker-Designed Data Institutions within Platform Hegemony. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, N...

    2023

  116. [2024]

    At the end of the day, I am accountable

    "At the end of the day, I am accountable": Gig Workers’ Self-Tracking for Multi-Dimensional Accountability Management. In Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24). Association for Computing Machinery, New York, NY, U...

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.