Knowledge Mobilization as a Tool of Institutional Governance: Exploring Academics’ Perceptions of “Going Public”
Bibliographic record
Abstract
In Canada there are growing discussions concerning the role of publicly funded universities and the impact of academic research. The integration of neoliberal practices and market rationalities place pressure on universities to “go public” in order to demonstrate relevance and accountability. Researchers are encouraged or even required to engage the public through knowledge mobilization activities. Our study provides an empirical analysis of knowledge mobilization in order to understand its perceived impact on public criminology, and more broadly the production and dissemination of criminological research. We argue that the institutional shift toward knowledge mobilization is perceived as a tool of institutional governance to demonstrate organizational accountability that shapes the production and dissemination of criminological knowledge.In Canada there are growing discussions concerning the role of publicly funded universities and the impact of academic research. The integration of neoliberal practices and market rationalities place pressure on universities to “go public” in order to demonstrate relevance and accountability. Researchers are encouraged or even required to engage the public through knowledge mobilization activities. Our study provides an empirical analysis of knowledge mobilization in order to understand its perceived impact on public criminology, and more broadly the production and dissemination of criminological research. We argue that the institutional shift toward knowledge mobilization is perceived as a tool of institutional governance to demonstrate organizational accountability that shapes the production and dissemination of criminological knowledge.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".