Improving the Accessibility of Federal Graduate Research Awards in Canada
Bibliographic record
Abstract
Canadian federal graduate research awards provide graduate students with support that impacts both their experience during their degree and their future career progression. Obtaining federal funding during graduate education qualifies students for additional awards, provides financial security, and increases their research independence. However, the number and value of awards have remained unchanged for almost two decades and the evaluation and eligibility criteria are not designed to encourage applications from students from historically underrepresented groups (URGs). The three federal research funding agencies (the Tri-Agency) have recently released an Equity, Diversity, and Inclusion (EDI) Action Plan to better support early-career individuals from these groups, with a commitment to “identify and address barriers to equitable participation of members from underrepresented groups” (Initiative 1.2.2) and increase participation of URGs in the post-secondary research system (Objective 2). In this memo, we propose three changes to broaden the eligibility and evaluation of federal student awards, as well as increase and standardize the award values. Ultimately, these recommendations will reduce the barriers faced by URGs in applying for and obtaining these awards in a manner not currently addressed by the Tri-Agency’s EDI plan.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.003 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".