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Open science interventions proposed or implemented to assess researcher impact: a scoping review

2023· review· en· W4387874040 on OpenAlex
Mona Ghannad, Anna Catharina Vieira Armond, Jeremy Y. Ng, Ana Patricia Ayala, Hassan Khan, Maura R. Grossman, Gordon V. Cormack, Ba’ Pham, Mariska Leeflang, Patrick M. Bossuyt, Karim M. Khan, Clare L. Ardern, David Moher

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueF1000Research · 2023
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of OttawaUniversity of WaterlooUniversity of TorontoOttawa HospitalUniversity of British Columbia
Fundersnot available
KeywordsRigourOpen sciencePsychological interventionPromotion (chess)BiomedicinePolitical sciencePsychologyEngineering ethicsMedical educationManagement scienceMedicineEngineeringNursing

Abstract

fetched live from OpenAlex

<ns3:p>Background Several open science-promoting initiatives have been proposed to improve the quality of biomedical research, including initiatives for assessing researchers’ open science behaviour as criteria for promotion or tenure. Yet there is limited evidence to judge whether the interventions are effective. This review aimed to summarise the literature, identifying open science practices related to researcher assessment, and map the extent of evidence of existing interventions implemented to assess researchers and research impact. Methods A scoping review using the Joanna Briggs Institute Scoping Review Methodology was conducted. We included all study types that described any open science practice-promoting initiatives proposed or implemented to assess researchers and research impact, in health sciences, biomedicine, psychology, and economics. Data synthesis was quantitative and descriptive. Results Among 18,020 identified documents, 27 articles were selectedfor analysis. Most of the publications were in the field of health sciences (n = 10), and were indicated as research culture, perspective, commentary, essay, proceedings of a workshop, research article, world view, opinion, research note, editorial, report, and research policy articles (n = 22). The majority of studies proposed recommendations to address problems regarding threats to research rigour and reproducibility that were multi-modal (n = 20), targeting several open science practices. Some of the studies based their proposed recommendations on further evaluation or extension of previous initiatives. Most of the articles (n = 20) did not discuss implementation of their proposed intervention. Of the 27 included articles, 10 were cited in policy documents, with The Leiden Manifesto being the most cited (104 citations). Conclusion This review provides an overview of proposals to integrate open science into researcher assessment. The more promising ones need evaluation and, where appropriate, implementation. Study registration https://osf.io/ty9m7</ns3:p>

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.

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
gemmaMetaresearchOpen science
Domain: Incentives · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptMetaresearchOpen science
Domain: Evaluation · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.624
metaresearch head score (Gemma)0.431
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Bibliometrics, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6240.431
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0180.008
Bibliometrics0.0060.045
Science and technology studies0.0010.001
Scholarly communication0.0160.001
Open science0.0520.025
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1170.068

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 head0.993
GPT teacher head0.839
Teacher spread0.155 · 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