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Record W2302335834 · doi:10.1136/bmj.i1468

Reinventing qualitative research

2016· letter· en· W2302335834 on OpenAlexaff
Gavin J. Andrews

Bibliographic record

VenueBMJ · 2016
Typeletter
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsData scienceComputer scienceWorld Wide WebInformation retrievalMedicine

Abstract

fetched live from OpenAlex

I agree that The BMJ should not actively exclude qualitative research based on metrics,1 but what we need to consider is how qualitative research might be reinvented. As McCormack explains,2 much of it is currently, using a sports analogy, formulaic “post-game” interrogation. We (researchers) go all out and find “the player” (disadvantaged patient) during a break from “the game” (illness episode) …

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)0.799
metaresearch head score (Gemma)0.849
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.799
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7990.849
Meta-epidemiology (narrow)0.0040.006
Meta-epidemiology (broad)0.0090.008
Bibliometrics0.0120.008
Science and technology studies0.0270.169
Scholarly communication0.0440.120
Open science0.0270.080
Research integrity0.1310.171
Insufficient payload (model declined to judge)0.0160.014

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 head0.529
GPT teacher head0.679
Teacher spread0.150 · 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
Domainnot available
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

Citations0
Published2016
Admission routes1
Has abstractyes

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