From Policy to Practice: The Evolution of SSHRC Application Processes, 1979-Present
Bibliographic record
Abstract
Drawing on a case study of Social Sciences and Humanities Research Council (SSHRC) grant application procedures between 1979 and 2023, this article highlights the shifting landscape of Canadian federal research funding. Through an analysis of application procedures and requirements, the article argues that changes in the SSHRC grant process reflect broader shifts in government priorities and financial contexts. While complexity and competition have been consistent factors dating back to the early 1980s, each change to the application process illustrates changing federal priorities and values. The article argues that SSHRC’s processes have evolved alongside broader trends in public accountability. This historical understanding helps to provide necessary context for contemporary debates around federal grant funding in Canada.
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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 | 0.099 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.033 | 0.033 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".