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Record W2804405028 · doi:10.1503/cmaj.180545

Coming soon: <i>CMAJ</i> policy on data sharing

2018· editorial· en· W2804405028 on OpenAlexvenueno aff
Diane Kelsall

Bibliographic record

VenueCanadian Medical Association Journal · 2018
Typeeditorial
Languageen
FieldDecision Sciences
TopicDiverse academic research themes
Canadian institutionsnot available
Fundersnot available
KeywordsData sharingStatement (logic)Computer scienceData scienceOperations researchPolitical scienceMedicineAlternative medicineLawEngineering

Abstract

fetched live from OpenAlex

In an editorial last year,[1][1] we described CMAJ ’s new policy on data sharing that will come into effect on July 1, 2018. After that date, authors of any research study submitted to the journal will need to supply a data-sharing statement, and those using shared data will need to acknowledge

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.063
metaresearch head score (Gemma)0.254
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.992
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.254
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0050.004
Science and technology studies0.0130.011
Scholarly communication0.0240.013
Open science0.0080.006
Research integrity0.0480.083
Insufficient payload (model declined to judge)0.0320.028

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 head0.139
GPT teacher head0.447
Teacher spread0.309 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainReproducibility
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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