Pith Number
pith:M4HSGWU4
pith:2022:M4HSGWU4XFI4OYUZ6OXICVUFCC
not attested
not anchored
not stored
refs pending
Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruning
arxiv:2207.02463 v1 · 2022-07-06 · cs.CL
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{M4HSGWU4XFI4OYUZ6OXICVUFCC}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
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claim
4
Citations
5
Replications
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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.
Cited by
Receipt and verification
| First computed | 2026-07-05T04:38:03.473972Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
670f235a9cb951c76299f3ae81568510859492ff8562a4e057fc793d95281ad8
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/M4HSGWU4XFI4OYUZ6OXICVUFCC \
| 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: 670f235a9cb951c76299f3ae81568510859492ff8562a4e057fc793d95281ad8
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "f46a888dc3b0dc4b1917340dd3dc72bb6957d28de30a2c0c8ebc472b23113116",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CL",
"submitted_at": "2022-07-06T06:20:35Z",
"title_canon_sha256": "9ad76bbfe76507f5fa3e2f436c412e884b160f792b52fecb1e1a69241e20f765"
},
"schema_version": "1.0",
"source": {
"id": "2207.02463",
"kind": "arxiv",
"version": 1
}
}