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Record W2782848721 · doi:10.1515/ling-2017-0032

Reproducible research in linguistics: A position statement on data citation and attribution in our field

2017· article· en· W2782848721 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLinguistics · 2017
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsAttributionLinguisticsStatement (logic)CitationField (mathematics)Applied linguisticsQuantitative linguisticsAuthorship attributionPosition statementClinical linguisticsPsychologyComputer scienceSociologyPhilosophySocial psychologyLibrary scienceMedicine

Abstract

fetched live from OpenAlex

Abstract This paper is a position statement on reproducible research in linguistics, including data citation and attribution, that represents the collective views of some 41 colleagues. Reproducibility can play a key role in increasing verification and accountability in linguistic research, and is a hallmark of social science research that is currently under-represented in our field. We believe that we need to take time as a discipline to clearly articulate our expectations for how linguistic data are managed, cited, and maintained for long-term access.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.187
GPT teacher head0.470
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it