Paper Citation Record · LEDGER

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy

As of 9 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2502.06150.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.06150 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:37:34.845464Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy6
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2c470cc6-ddac-4ac7-910b-ae9f23ac1873 · outbound

This paper cites A contrastive topic-aware attentive framework with label encodings for post-disaster resource classification,.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy A contrastive topic-aware attentive framework with label encodings for post-disaster resource classification,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:37:35.217493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b58674c-8b74-4f71-ae99-5ccca02e4eb6 · outbound

This paper cites Crowdsourcing for machine learning in public health surveillance: Lessons learned from amazon mechanical turk,.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Crowdsourcing for machine learning in public health surveillance: Lessons learned from amazon mechanical turk,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:37:35.201814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 880a328b-909c-4d97-baa1-7b9904b5f8f7 · outbound

This paper cites Who broke amazon mechanical turk? an analysis of crowdsourcing data quality over time,.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Who broke amazon mechanical turk? an analysis of crowdsourcing data quality over time,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:37:35.185970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c195c93e-b984-4522-9adf-80198bd720ba · outbound

This paper cites The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T16:37:34.791518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59598503-96b1-448d-8f61-58a2fef334e6 · outbound

This paper cites Language Models are Few-Shot Learners.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Language Models are Few-Shot Learners

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T16:37:34.797008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f13c495-0800-4826-836c-f907e2126797 · outbound

This paper cites Language models are unsupervised multitask learners,.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Language models are unsupervised multitask learners,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T16:37:34.802150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bf12a7e-acd4-4d3a-98de-c3f79ed19a96 · outbound

This paper cites an unresolved cited work.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:37:35.160792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 980645f7-5499-479a-af4d-577f75dc5645 · outbound

This paper cites ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T16:37:34.817466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7557e61-f8f7-4b11-b907-8aab849a5706 · outbound

This paper cites Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T16:37:34.822700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7aa6a268-af44-43ad-a4a7-e2a7d92bb99b · outbound

This paper cites Chatgpt outperforms crowd workers for text-annotation tasks,.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Chatgpt outperforms crowd workers for text-annotation tasks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:37:35.145725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dbc09b0e-36c3-42c0-98ee-6e9b89620671 · outbound

This paper cites Physical activity, sedentary behavior, and sleep on twitter: Multicountry and fully labeled public data set for digital public health surveillance research,.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Physical activity, sedentary behavior, and sleep on twitter: Multicountry and fully labeled public data set for digital public health surveillance research,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:37:35.130465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 703aec85-13fa-45d5-9c80-2fb542c58d63 · outbound

This paper cites [Online].

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy [Online]

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:37:35.113988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 529ce6db-b003-4449-9183-bc5f6f0d6699 · outbound

This paper cites Evaluation of openai o1: Opportuni- ties and challenges of agi,.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Evaluation of openai o1: Opportuni- ties and challenges of agi,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T16:37:34.841175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df1d08ca-e9ca-4dca-8949-8a5ce3efe832 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy LoRA: Low-Rank Adaptation of Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T16:37:34.845464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91539db9-b48d-40c4-8e3e-43fc1abfe967 · outbound

This paper cites Is ChatGPT a General-Purpose Natural Language Processing Task Solver?.

Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T16:37:34.812533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.