Inventory of Current Approaches, Initiatives, and Practices in Canadian Academic Institutions to Foster the Engagement of Social Sciences, Humanities and Arts in the Science-Policy Interface
Bibliographic record
Abstract
This study presents a national inventory of how Canadian universities engage Social Sciences, Humanities, and Arts (SSHA) disciplines in the science-policy interface (SPI). Using a digital humanities methodology, the research team scanned departmental websites at 90 public universities, identifying 4,032 distinct policy-related activities. These were categorized into individual initiatives, punctual activities, and partnership-building efforts. Results show a system heavily weighted toward individual engagement (68%) and short-term activities (21%), with only 12% representing sustained, institutionalized partnerships. The analysis reveals disciplinary concentration in Public Health/Public Policy, Business/Economics, Sociology, Criminology and Law, and Political Science/International Studies, which together account for over two-thirds of all entries. Provincial disparities are significant: British Columbia dominates in total entries, but Québec leads in structured partnerships and government collaborations. Despite widespread SSHA participation across disciplines, partnerships with Indigenous communities, municipal governments, and private actors remain rare. Most partnership-building efforts are aimed at social development and education, with little focus on science policy or reconciliation. The findings expose the limits of a decentralized, individually driven engagement model and highlight the need for institutional infrastructure, funding mechanisms, and evaluative frameworks aligned with collaborative, long-term policy engagement. The study calls for a strategic shift: from rewarding output-based dissemination to enabling embedded, co-productive policy work. Without such realignment, SSHA contributions will remain fragmented, under-leveraged, and structurally peripheral to public decision-making, despite their proven relevance. The inventory provides a baseline for reforming how universities support SSHA engagement at the SPI and for reimagining their role in shaping public policy.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.025 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".