Bibliographic record
Abstract
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Library, for published literature, and OpenGrey, GoogleScholar, arXiv.org, medRxiv.org and ClinicalTrials.gov for unpublished literature.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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.981 | 0.724 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".