MétaCan
Menu
Back to cohort
Record W199830221

The missing data dilemma.

2009· article· fr· W199830221 on OpenAlexaff
Mae Squires, Ann E. Tourangeau

Bibliographic record

VenuePubMed · 2009
Typearticle
Languagefr
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsKingston General Hospital
Fundersnot available
KeywordsDilemmaMissing dataComputer scienceData scienceEpistemologyMachine learningPhilosophy
DOInot available

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.181
metaresearch head score (Gemma)0.659
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.959

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1810.659
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0230.019
Science and technology studies0.0030.007
Scholarly communication0.0080.018
Open science0.0060.006
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0240.014

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 head0.505
GPT teacher head0.461
Teacher spread0.044 · 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
DomainMethods
GenreEmpirical

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
Published2009
Admission routes1
Has abstractno

Explore more

Same venuePubMedSame topicMedical Coding and Health InformationFrench-language works237,207