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Paper Citation Record · LEDGER

Out of One, Many: Using Language Models to Simulate Human Samples

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2209.06899.

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

pith.paper-citation-record.v1
2209.06899 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:00:10.259911Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

92
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 89ed21fa-de56-4daa-9062-8a0a456295fb · inbound

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators cites this paper.

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators Out of One, Many: Using Language Models to Simulate Human Samples

Reference 137

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T11:13:04.763401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-12T11:13:04.597432Z digest=sha256:12f21196def86e48943cd9c9f4f862150392b72c4d1b7266ee80bfe86e4d03a5

Observation 08569814-a49b-4695-92ac-52314cefb949 · inbound

The Effect of State Representation on LLM Agent Behavior in Dynamic Routing Games cites this paper.

The Effect of State Representation on LLM Agent Behavior in Dynamic Routing Games Out of One, Many: Using Language Models to Simulate Human Samples

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T00:00:10.259911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:00:10.259911Z digest=sha256:6a1d967cf5b5f8ae1612853be60f1bae9093ff6c51a18ea2b8d219738348702e

Observation 35f7564d-21c5-43ca-8562-1bda42a62b49 · inbound

Guidelines for Empirical Studies in Software Engineering involving Large Language Models cites this paper.

Guidelines for Empirical Studies in Software Engineering involving Large Language Models Out of One, Many: Using Language Models to Simulate Human Samples

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:02:52.708252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T22:02:36.307598Z digest=sha256:79e88aa268b66121f85f4be19963e2c8cc3af649e16c84eae15343ee810b42a1

Observation 8d252832-0c1b-40d7-9b6f-5d17c5216f15 · inbound

Guidelines for Empirical Studies in Software Engineering involving Large Language Models cites this paper.

Guidelines for Empirical Studies in Software Engineering involving Large Language Models Out of One, Many: Using Language Models to Simulate Human Samples

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:20:32.027600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-25T08:18:18.448122Z digest=sha256:45c3b0df8bc08dae4d63ab5b1cdac5a89ade75636d28eb3a35c3f1a83e8d9400

Observation 1dc092e8-19a5-4c9e-8f1b-fdf4e73f2bcc · inbound

Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation cites this paper.

Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation Out of One, Many: Using Language Models to Simulate Human Samples

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-18T18:46:44.890524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T18:46:39.488463Z digest=sha256:4ab0220318b547242be285d635a840590924e6585c8d4101a62deee88e2c89c3

Observation 49e55021-a66b-4e6f-b486-4047ed5adc78 · inbound

Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimization cites this paper.

Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimization Out of One, Many: Using Language Models to Simulate Human Samples

Reference 1

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T13:13:27.567995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T13:05:15.701729Z digest=sha256:64756ef4ca1cfdc594f1de680c41b6f75b4a629fd8dc12fd11ddd43e02951d60

Observation b15607dd-0cba-4080-9451-de3354819a58 · inbound

Child-directed speech facilitates production, not comprehension, in BabyLMs cites this paper.

Child-directed speech facilitates production, not comprehension, in BabyLMs Out of One, Many: Using Language Models to Simulate Human Samples

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-01T21:06:14.341245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T17:28:32.405210Z digest=sha256:bb6aaea170ec74fa796d6af784d1db8ed6ff028a75cee28ef959e19264528ad0

Observation 4e8f5077-fddd-4897-b525-268d49fd82b3 · inbound

Characterizing initial human-AI proof formalization workflows cites this paper.

Characterizing initial human-AI proof formalization workflows Out of One, Many: Using Language Models to Simulate Human Samples

Reference 218

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T04:06:34.889335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T09:29:50.282874Z digest=sha256:6eb83b6030018ac60364510caed20864c0e38cfd307335f56625bd0af682fd01

Observation 587187d6-4c3f-4521-be65-6a1d979a0d6c · inbound

Marginal Alignment Does Not Guarantee Joint-Distribution Fidelity: An Official-Reference Audit of Nemotron-Personas-Korea with Cross-Locale Replication cites this paper.

Marginal Alignment Does Not Guarantee Joint-Distribution Fidelity: An Official-Reference Audit of Nemotron-Personas-Korea with Cross-Locale Replication Out of One, Many: Using Language Models to Simulate Human Samples

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T19:05:00.239029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-30T19:04:36.443335Z digest=sha256:33c21e515be5c860749cff490e181c84e489fb59039c684c0672496666d982ae