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Record W7154873752

The Development of PRITEM Reporting Guideline for Mapping Reviews

2024· other· W7154873752 on OpenAlexaboutno aff
Yanfei Li, Elizabeth Ghogomu, Marie Gaarder, Hanan Khalil, Xiuxia Li, Promise M Nduku, Fiona Campbell, Howard White, Liangying Hou, XU HUI, Fenfen e, Vivian Welch, Kehu Yang

Bibliographic record

VenueOpen MIND · 2024
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDelphi methodMultidisciplinary approachDelphiResource (disambiguation)Best practiceSystematic reviewGuidelineDissemination
DOInot available

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearch
Domain: Reporting · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.553
metaresearch head score (Gemma)0.729
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.447
Threshold uncertainty score0.551

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5530.729
Meta-epidemiology (narrow)0.0050.010
Meta-epidemiology (broad)0.0100.023
Bibliometrics0.0430.037
Science and technology studies0.0060.006
Scholarly communication0.0230.021
Open science0.0240.024
Research integrity0.0210.026
Insufficient payload (model declined to judge)0.0130.022

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 head0.300
GPT teacher head0.453
Teacher spread0.153 · 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

Labeled directly by 2 models reading the full record.

Study designNot applicable
DomainReporting
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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