Pith. sign in

Paper Citation Record · LEDGER

In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.11190.

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

pith.paper-citation-record.v1
2407.11190 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:44:27.869309Z

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

1
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 d8332116-f08d-453e-aab1-f2e0dfb56119 · inbound

Subjective Perspectives within Learned Representations Predict High-Impact Innovation cites this paper.

Subjective Perspectives within Learned Representations Predict High-Impact Innovation In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T10:44:27.869309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:44:27.869309Z digest=sha256:2c26be07e04523df72f7d79a15f29113d5454bd7e6d15655303022774734b30e

Observation 121e4193-a276-407f-bc70-832d0c27aef0 · inbound

Multi-Actor Generative Artificial Intelligence as a Game Engine cites this paper.

Multi-Actor Generative Artificial Intelligence as a Game Engine In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T18:28:23.575711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:28:23.575711Z digest=sha256:bb86904bc13e95ccaecf583fab1dd246a0d2354471ef0785aacca1840f00824d

Observation 77278dd8-f8ac-43fa-a731-7b5aaf58032b · inbound

Algorithmic Tradeoffs, Applied NLP, and the State-of-the-Art Fallacy cites this paper.

Algorithmic Tradeoffs, Applied NLP, and the State-of-the-Art Fallacy In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models

Reference 83

Resolution
unresolved
no resolver link, observed 2026-08-04T21:10:32.687797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T21:10:32.687797Z digest=sha256:bf174173754a65f35758c1e474f4609dc915248da849cefd16e70ffba8896d5e

Observation 48001319-a995-4b7a-94cc-6f0008f7a7d2 · inbound

Not-quite-human tastes: the stylized omnivorousness of LLM survey surrogates cites this paper.

Not-quite-human tastes: the stylized omnivorousness of LLM survey surrogates In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T06:24:18.636172Z

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=2026-06-30T06:15:48.842327Z digest=sha256:a1e74c735d5c542b9fab31129cae2cebeb4d75c3e0df90b0ba7aa433023ebe51

Observation 04497c3c-8b1a-4e0c-8344-11b01c05b73e · inbound

Free-form Association Tasks Reveal Stereotype Hallucination in Large Language Models cites this paper.

Free-form Association Tasks Reveal Stereotype Hallucination in Large Language Models In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-07-01T13:15:45.440059Z

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=2026-07-01T00:57:47.699645Z digest=sha256:7955d90b56a466694b36ebb4cd8cad7a84267a0024acf42f2e84e170aff0a56b