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Paper Citation Record · LEDGER

Few-shot Learning for Topic Modeling

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2104.09011.

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

pith.paper-citation-record.v1
2104.09011 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:43:27.668957Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:40:26.923157Z

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 532d8de0-f009-4360-bb19-200922fd2f39 · inbound

Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling cites this paper.

Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling Few-shot Learning for Topic Modeling

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T05:43:27.668957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:43:27.668957Z digest=sha256:7b55d98d69eed063aa1b24abebc7cf580758723a653b3d31071a6f8e6e31fbf2

Observation 50f96ec6-6da4-46b9-87ce-8bc61c9476b6 · inbound

Meta-learning Representations for Learning from Multiple Annotators cites this paper.

Meta-learning Representations for Learning from Multiple Annotators Few-shot Learning for Topic Modeling

Reference 2015

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:40:26.927662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:40:25.106610Z digest=sha256:142fa92543a1c8bf97dd048c9b612902f46fb27b808c39f5f47db0c3f1dab86a