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Record W4384626449 · doi:10.1111/1471-0528.17528

We can predict, but validation and implementation remain a challenge

2023· article· en· W4384626449 on OpenAlexaff
Jeffrey N. Bone

Bibliographic record

VenueBJOG An International Journal of Obstetrics & Gynaecology · 2023
Typearticle
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsPsychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Linked article: This is a mini commentary on Venkatesh et al., pp.1531–1540 in this issue. To view this article visit https://doi.org/10.1111/1471‐0528.17572 .

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)0.207
metaresearch head score (Gemma)0.517
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.207
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2070.517
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.002
Science and technology studies0.0040.029
Scholarly communication0.0120.041
Open science0.0080.011
Research integrity0.0150.047
Insufficient payload (model declined to judge)0.0260.014

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 head0.126
GPT teacher head0.433
Teacher spread0.307 · 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 designTheoretical or conceptual
Domainnot available
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

Citations1
Published2023
Admission routes1
Has abstractyes

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Same venueBJOG An International Journal of Obstetrics & GynaecologySame topicAdvanced Causal Inference TechniquesFrench-language works237,207