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ARXIV:2602.22359 · NLP TECHNIQUES · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2602.22359NLP TECHNIQUESSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
Using GPT-5 for thick citation context analysis to enhance interpretative readings in academic works.
Opportunity summary
Pain Using GPT-5 for thick citation context analysis to enhance interpretative readings in academic works.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Using GPT-5 for thick citation context analysis to enhance interpretative readings in academic works. It foregrounds prompt-sensitivity analysis as a methodological issue by varying prompt scaffolding and framing in a balanced 2x3 design.
This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up typological labels. It foregrounds…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single…
NLP Techniques moved forward this cycle; last verified April 2026. Public score 2.0/10.
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Using GPT-5 for thick citation context analysis to enhance interpretative readings in academic works.
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10.48550/arXiv.2602.22359Using GPT-5 for thick citation context analysis to enhance interpretative readings in academic works.
Abstract
This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up typological labels. It foregrounds prompt-sensitivity analysis as a methodological issue by varying prompt scaffolding and framing in a balanced 2x3 design. Using footnote 6 in Chubin and Moitra (1975) and Gilbert's (1977) reconstruction as a probe, I implement a two-stage GPT-5 pipeline: a citation-text-only surface classification and expectation pass, followed by cross-document interpretative reconstruction using the citing and cited full texts. Across 90 reconstructions, the model produces 450 distinct hypotheses. Close reading and inductive coding identify 21 recurring interpretative moves, and linear probability models estimate how prompt choices shift their frequencies and lexical repertoire. GPT-5's surface pass is highly stable, consistently classifying the citation as "supplementary". In reconstruction, the model generates a structured space of plausible alternatives, but scaffolding and examples redistribute attention and vocabulary, sometimes toward strained readings. Relative to Gilbert, GPT-5 detects the same textual hinges yet more often resolves them as lineage and positioning than as admonishment. The study outlines opportunities and risks of using LLMs as guided co-analysts for inspectable, contestable interpretative CCA, and it shows that prompt scaffolding and framing systematically tilt which plausible readings and vocabularies the model foregrounds.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Preparing verified analysis
Dimensions overall score 2.0
PROBLEM
Using GPT-5 for thick citation context analysis to enhance interpretative readings in academic works. It foregrounds prompt-sensitivity analysis as a methodological issue by varying prompt scaffolding and framing in a balanced 2x3 design.
METHOD
This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up typological labels. It foregrounds prompt-sensitivity analysis as a methodolo...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up ty...
WHY NOW
NLP Techniques moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Using GPT-5 for thick citation context analysis to enhance interpretative readings in academic works. It foregrounds prompt-sensitivity analysis as a methodological issue by varying prompt scaffolding and framing in a balanced 2x3 design.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up typological labels. It foregrounds prompt-sensitivity analysis as a methodological issue by varying prompt scaffolding and framing in a balanced 2x3 design.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up typological labels.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
NLP Techniques moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Using GPT-5 for thick citation context analysis to enhance interpretative readings in academic works.
Segment
NLP Techniques
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Commercial read
2.0/10 public viability
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CITED BY
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reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 0 sources, 33% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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People
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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