Paper Citation Record · LEDGER

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts

As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 5 inbound Pith citation observations for arXiv:2412.01934.

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

pith.paper-citation-record.v1
2412.01934 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:07:18.921234Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:37:34.652479Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T03:41:00.097378Z

Reference resolution

21 of 21 outbound references displayed

verified exact1
verified fuzzy13
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c39dc24c-5675-4c8f-9ba1-4ca00ebfd416 · outbound

This paper cites Measurement validity: A shared standard for qualitative and quantitative research.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Measurement validity: A shared standard for qualitative and quantitative research

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.288140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fbe8b7df-2012-443a-94d4-3069e3759372 · outbound

This paper cites Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.272376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 14083a97-f324-4841-a37a-2ead37f2be83 · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Quantifying Memorization Across Neural Language Models

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcbf329e-0bc2-4bd0-b442-d1c5c73dd838 · outbound

This paper cites The Files are in the Computer: On Copyright, Memorization, and Generative AI.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts The Files are in the Computer: On Copyright, Memorization, and Generative AI

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T00:07:18.837941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55143b66-1ee8-4359-8987-ce86df401d5c · outbound

This paper cites Feder Cooper, Yucheng Lu, Jessica Forde, and Christopher M De Sa.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Feder Cooper, Yucheng Lu, Jessica Forde, and Christopher M De Sa

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.257045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 660036fd-e4db-4a6c-9542-e39e62dbb9eb · outbound

This paper cites Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon Kleinberg, Siddhartha Sen, and Baobao Zhang.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon Kleinberg, Siddhartha Sen, and Baobao Zhang

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.241888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7fe3a9bf-9c5b-46bd-aad0-b9853521c3e9 · outbound

This paper cites Counterfactual risk assessments, evaluation, and fairness.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Counterfactual risk assessments, evaluation, and fairness

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.226237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a86b3be5-b190-4694-b3ea-406ae528a7dc · outbound

This paper cites an unresolved cited work.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:07:19.210491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a843699a-978c-4f6e-a321-fc82bec94769 · outbound

This paper cites Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T00:07:18.862879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed8c1381-36fe-40d8-a69c-f87f1f74af5e · outbound

This paper cites Optimization’s neglected normative commit- ments.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Optimization’s neglected normative commit- ments

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.194751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0ee33f30-f8c7-4e0e-abe9-38fa417ed0dd · outbound

This paper cites Vera Liao, Alexandra Olteanu, and Ziang Xiao.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Vera Liao, Alexandra Olteanu, and Ziang Xiao

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.179078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7343269e-8f5b-4349-96a0-e7229e8f3db9 · outbound

This paper cites The AI index 2024 annual report.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts The AI index 2024 annual report

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.163306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aae5bc0d-6d53-4a29-a0a4-2a02f902c24f · outbound

This paper cites Proxies: The cultural work of standing in.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Proxies: The cultural work of standing in

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.146762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 832b57a3-80fa-4950-810e-b2b1b749d3ca · outbound

This paper cites On the consistency of hyper-parameter selection in value-based deep reinforcement learning.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts On the consistency of hyper-parameter selection in value-based deep reinforcement learning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-12T00:07:18.998829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 61438396-1937-49ab-b95d-0b6ca466d5bf · outbound

This paper cites Dissecting racial bias in an algorithm used to manage the health of populations.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Dissecting racial bias in an algorithm used to manage the health of populations

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.128972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8e159abb-f153-40fa-8dbd-36fcdb4a0e5e · outbound

This paper cites Problem formulation and fairness.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Problem formulation and fairness

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.112986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9d60269-2530-4c83-8de0-9afda8739436 · outbound

This paper cites Are my deep learning systems fair? an empirical study of fixed-seed training.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Are my deep learning systems fair? an empirical study of fixed-seed training

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.096659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ecc6aa0d-c171-4890-83ca-17425aa11a7a · outbound

This paper cites an unresolved cited work.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:07:19.079953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e49b80a4-c4fe-485c-b3f7-93ee1879d874 · outbound

This paper cites Evaluating the Social Impact of Generative AI Systems in Systems and Society.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Evaluating the Social Impact of Generative AI Systems in Systems and Society

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T00:07:18.911374Z

Source-reported events for the cited work

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Observation 9e3742c6-89f0-4aa3-9922-2b0c0c8baed9 · outbound

This paper cites Multi-target multiplicity: Flexibility and fairness in target specification under resource constraints.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Multi-target multiplicity: Flexibility and fairness in target specification under resource constraints

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:07:19.064337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 177f72cc-7719-4287-8697-736e8d02fb5d · outbound

This paper cites Position: Measure Dataset Diversity, Don't Just Claim It.

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts Position: Measure Dataset Diversity, Don't Just Claim It

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T00:07:18.921234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6337746e-a493-41dc-894d-2499a7b1b4f3 · inbound

Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge cites this paper.

Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T18:37:34.652479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cc1cb86-35dc-4ee9-892a-74586c65d2ac · inbound

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects cites this paper.

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c6f5834-0c92-4a10-9071-75a89bfddc77 · inbound

Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances cites this paper.

Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T19:47:42.986492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20920e93-0cab-456f-8dd1-3171ea9fd02d · inbound

Making AI Evaluation Deployment Relevant Through Context Specification cites this paper.

Making AI Evaluation Deployment Relevant Through Context Specification A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:50:05.150614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e3dcc03d-55a4-40c1-9081-293036925e15 · inbound

Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions cites this paper.

Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T03:41:00.101171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.