Pith Number

pith:OYNOESOS

pith:2026:OYNOESOSEW3NT4XIGY3NJKCZTZ

not attested not anchored not stored refs resolved

Mapping the Stochastic Penal Colony

Robert Grimm

Content moderation banishes users to a stochastic penal colony through the constant threat of account suspension.

arxiv:2602.00033 v2 · 2026-01-18 · cs.CY

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\usepackage{pith}
\pithnumber{OYNOESOSEW3NT4XIGY3NJKCZTZ}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

While substantially different, all three feature the pervasive threat of account suspension, which banishes users to the stochastic penal colony.

C2weakest assumption

That Foucault's historical penal model can be directly reworked for algorithmic content moderation without losing essential accuracy, and that the three chosen case studies adequately represent broader platform practices.

C3one line summary

Content moderation operates as a stochastic penal colony that banishes users through the constant threat of account suspension, shown via auto-ethnographic case studies of Twitter, OpenAI DALL-E 2, and Pinterest.

References

119 extracted · 119 resolved · 1 Pith anchors

[1] Access Now, ACLU Foundation of Northern California, ACLU Foundation of Southern California, Article 19, Brennan Center for Justice, Center for Democracy & Technology, Electronic Frontier Foundation, G 2021

[2] Robert Aldrich. 2010. The French Overseas Empire and Its Contemporary Legacy.European History Quarterly40, 1 (Jan. 2010), 97–108. https: //doi.org/10.1177/0265691409351339 2010 · doi:10.1177/0265691409351339

[3] 2018.A Global History of Convicts and Penal Colonies 2018

[4] Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine Bias.ProPublica(May 2016). https://www.propublica.org/article/ machine-bias-risk-assessments-in-criminal-sentencing 2016

[5] Anonymous. 2016. Incident 37: Female Applicants Down-Ranked by Amazon Recruiting Tool.Artificial Intelligence Incident Database(Aug. 2016). https://incidentdatabase.ai/cite/37/ 2016

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:45:05.640459Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

761ae249d225b6d9f2e83636d4a8599e697494e4466d9718c680d58d5b2419bf

Aliases

arxiv: 2602.00033 · arxiv_version: 2602.00033v2 · doi: 10.48550/arxiv.2602.00033 · pith_short_12: OYNOESOSEW3N · pith_short_16: OYNOESOSEW3NT4XI · pith_short_8: OYNOESOS

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OYNOESOSEW3NT4XIGY3NJKCZTZ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 761ae249d225b6d9f2e83636d4a8599e697494e4466d9718c680d58d5b2419bf

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "cd28f6e47af934850192a3c872cf5f2d545e868d746296aff73513a4f4bb9720",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.CY",
    "submitted_at": "2026-01-18T16:22:59Z",
    "title_canon_sha256": "5f41e5b5ee53d763a0e17398c1f8c636e8326af3ee47d8204f2127dce9f7c767"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.00033",
    "kind": "arxiv",
    "version": 2
  }
}