Mobilizing Knowledge in the Humanities and Social Sciences: Exploring Competing Articulations of Openness in Policy and Practice
Bibliographic record
Abstract
Knowledge mobilisation (KMb) is a policy discourse and framework used by major Canadian research funding bodies to promote and monitor the efficiency of knowledge transfer between the university and society. Since 2009, most humanities and social science researchers (HSS) researchers applying for federal funding must complete a KMb module that describes their intended collaborators and non-academic audiences, planned outreach activities and metrics to gauge their success. The ideals of public engagement set out in KMb policy are worthy ones for scholars to strive towards. The framework can also provide legitimation for a diverse range of research practices, relationships, and outputs. Applicants must think about sharing their work throughout the research process rather than simply at its end. This introduces a more expansive understanding of the relations of knowledge producers and their publics than is found in Canadian open access policies and mandates. Many practices commonly understood as open research, such as data sharing, diamond open access publishing, or sharing via blogs or podcasts, would be considered knowledge mobilisation activities, as would practices of community-engaged research or knowledge co-production. Thus KMb policy governs much of the making public of humanities research in Canada. However, it embodies conflicting ideas about the value of shared knowledge. Its emphasis on knowledge as transferable imposes temporal, material, and cognitive restrictions on scholarship. Critics of KMb dismiss it as performative, a tool of institutional governance, or argue that quantifies research as a return on investment. The critiques and possibilities of knowledge mobilisation policy offer insight into wider contemporary struggles over the meaning of openness for humanities and social science research. This paper explores its impact on Canadian HSS scholars in relation to critical debates about changing relations of knowledge, labour, and value in humanities scholarly communication.
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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 | 0.152 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.037 | 0.187 |
| Scholarly communication | 0.067 | 0.048 |
| Open science | 0.007 | 0.049 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".