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Record W4233669273 · doi:10.1503/cmaj.114-0034

Inaccurate numbers in Salon article

2014· article· en· W4233669273 on OpenAlexaffvenueabout
Chris Ma

Bibliographic record

VenueCanadian Medical Association Journal · 2014
Typearticle
Languageen
FieldMathematics
TopicMathematics and Applications
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsSalonComputer scienceData scienceInformation retrievalWorld Wide WebArt historyArt

Abstract

fetched live from OpenAlex

A Salon article[1][1] published in the Apr. 15, 2014, issue of CMAJ indicates that “[a]lmost 150 schools in over 60 countries are training Canadian students.” The figures should have appeared as 130 schools and 30 countries.[2][2] CMAJ apologizes for any confusion this oversight may have caused

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)0.013
metaresearch head score (Gemma)0.175
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.331

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.175
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0060.005
Scholarly communication0.0100.009
Open science0.0020.005
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0990.084

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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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
DomainReporting
GenreCommentary

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

Citations4
Published2014
Admission routes3
Has abstractyes

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