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Paper Citation Record · LEDGER

Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.15626.

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

pith.paper-citation-record.v1
2409.15626 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:32:11.201581Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T08:38:10.336891Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9a5dc804-1966-4fd3-954c-26247a70e8b8 · inbound

Potential and Perils of Large Language Models as Judges of Unstructured Textual Data cites this paper.

Potential and Perils of Large Language Models as Judges of Unstructured Textual Data Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-10T20:32:11.201581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:32:11.201581Z digest=sha256:1ecff0cb440152b2a6d501b9bf2d207fc1c390011c8f0542c01a218168717805

Observation 7a6be07b-bd6c-4fd7-958b-1b7a2e1acd95 · inbound

Restructure This: Using AI to Restructure Onboarding Documents to Reduce Cognitive Overload cites this paper.

Restructure This: Using AI to Restructure Onboarding Documents to Reduce Cognitive Overload Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling

Reference 106

Resolution
verified exact
arxiv_id, observed 2026-05-20T08:38:10.339432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-20T08:37:41.455802Z digest=sha256:0bc091dd3543e9adbf42de3672d151765fc7362044c689f141b7c1ba0863ce81