What goes in a funder’s Narrative CV?: A Scoping Review
Bibliographic record
Abstract
Narrative Curriculum Vitae (NCVs) are a type of CV focusing on written descriptions of researchers' skills, experiences, collaborations, and achievements, which seek to promote more equitable and responsible research assessments. Despite an apparent shift by funding organizations towards the use of NCVs to reassess how researchers are evaluated, the extent of current NCV adoption is unclear. Therefore, we conducted a scoping review to answer the following: 1. Which research funding agencies currently provide NCVs templates for their applicants? and 2. What are the characteristics of said NCV templates? To this end, we employed grey literature searches to identify all existing NCV templates provided by research funding organizations. Our findings highlight several key insights. First, number of funders currently requiring NCVs remains low overall, although national granting agencies are among the early adopters. Second, some funders do not provide formal guidance on how to complete narrative CV's-this may create barriers to uptake. Third, among the NCV templates identified, there are structural commonalities, although there is little insight into the evaluation of NCVs. As interest in NCVs grows, addressing these gaps will be essential to realizing their potential as a fairer and more holistic tool for research assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.142 | 0.395 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".