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Paper Citation Record · LEDGER

Context Matters: A Strategy to Pre-train Language Model for Science Education

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2301.12031.

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

pith.paper-citation-record.v1
2301.12031 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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:59:51.424071Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T16:38:11.386667Z

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 d7bc7bf5-8ad1-4ab3-812c-35650ce7868a · inbound

Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research cites this paper.

Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research Context Matters: A Strategy to Pre-train Language Model for Science Education

Reference 83

Resolution
verified exact
arxiv_id, observed 2026-05-23T16:38:11.389962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-23T16:36:02.895613Z digest=sha256:c67c3c6679dbdbbd3eccbcbc1577a91ed7d2e380b1e21d2c3d94e4a912047a9b

Observation 30b2483d-9d72-4b24-9198-aefb2e0d0210 · inbound

Fine-tuning ChatGPT for Automatic Scoring of Written Scientific Explanations in Chinese cites this paper.

Fine-tuning ChatGPT for Automatic Scoring of Written Scientific Explanations in Chinese Context Matters: A Strategy to Pre-train Language Model for Science Education

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-10T20:59:51.424071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T20:59:51.424071Z digest=sha256:532f73910a7e7acee4d88709a54ea6703cd0023096b16d695e3b73864df2734a