Predictors of Tri-council Funding Among Nutrition Researchers in Canada
Bibliographic record
Abstract
Purpose: Barriers in research for women and dietitians have been documented. We sought to describe tri-council funding awarded within the nutrition discipline according to institution type, academic rank, gender, dietitian status, and primary research methods used. Methods: Using an online search methodology, faculty members with research appointments were identified from nutrition departments offering accredited dietetic programs and/or at Canada’s collective of research-intensive universities known as U15. All data regarding faculty members, their institutions, and funding were collected through publicly available websites and Scopus. Tri-council funding associated with the nominated principal investigator, from a 5-year period, 2013–2014 to 2017–2018, was extracted. Binary logistic regression was used to test for predictors of receiving any tri-council operating funds within the 5-year period. Results: Faculty members (n = 237) from 21 institutions were identified for inclusion. Those from U15 institutions, at the full professor rank, nondietitians, men, and those who engaged in primarily quantitative research methods (vs. qualitative or mixed-methods) were significantly more likely to hold any tri-council funding during the eligible period. Dietitians (n = 76) were significantly less likely to hold tri-council funding, independent of institution, rank, gender, and primary research methods utilized. Conclusions: The apparent under-funding of academic dietitians from federal tri-council sources requires exploration.
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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 | 0.021 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".