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Record W6887763514 · doi:10.17605/osf.io/fxdz4

Grant Resubmissions Scoping Review

2022· other· en· W6887763514 on OpenAlexaff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2022
Typeother
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCINAHLMEDLINEBibliometricsInclusion (mineral)PsycINFOGrey literatureBiomedicineAudit

Abstract

fetched live from OpenAlex

Objective: To map the outcomes of resubmission policies for initially unsuccessful biomedical research grant applications to competitive funding agencies. Introduction: The initial success rate of grant applications in the biomedical sciences is falling and the number of resubmissions is rising. How funding agencies draft and apply policies on grant resubmission has implications for equity, diversity, and inclusion among applying scientists and for overall scientific innovation and progress. Inclusion criteria: Studies that measure outcomes and/or applicant demographics related to grant resubmission success are included. This review is limited to competitive funding agencies in biomedicine that make funding decisions based on peer review of grant applications, and does not include studies of only first-time applications. Methods: Using an existing scoping review methodology, we will systematically search: MEDLINE (Ovid), CINAHL (Ebsco), EMBASE (Ovid), Cochrane Central Registrar of Controlled Trials CENTRAL (Ovid), PsycInfo (Ebsco), Web of Science (University of British Columbia Institutional Access) , IEEE Xplore Digital Library, for published literature, and OpenGrey, GoogleScholar, arXiv.org, medRxiv.org and ClinicalTrials.gov for unpublished literature. In addition, a targeted Internet search will be performed to identify information in the grey literature. Two reviewers will independently screen abstracts and then full-text articles, identifying evidence for inclusion according to the criteria listed above. This review will report information including, but not limited to: demographic characteristics of the applicants to these competitions, process metrics (e.g., time to funding, volume of reapplications), or downstream effects of the resubmissions process (e.g., applicant career progress, likelihood of resubmitting, future funding or publication success). A preliminary search of MEDLINE, the Cochrane Database of Systematic Reviews, Open Science Framework, and JBI Evidence Synthesis was conducted and no active systematic reviews or scoping reviews on the topic were identified.

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)0.067
metaresearch head score (Gemma)0.337
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.337
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0100.011
Bibliometrics0.0450.042
Science and technology studies0.0030.002
Scholarly communication0.0100.010
Open science0.0060.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0260.005

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 head0.104
GPT teacher head0.425
Teacher spread0.321 · 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
DomainIncentives
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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