The Rhetoric and Reality of “Knowledge Mobilization”: Perspectives from the Research Front
Bibliographic record
Abstract
The Panel will discuss the emerging issue of “knowledge mobilization”, problematizing it as articulated by the Social Sciences and Humanities Research Council of Canada, and engaging the audience in critical discussion of the potential benefits and harms of mandated knowledge mobilization requirements linked to unspecified notions of the “public good”.Ce panel discutera du thème émergent de la « mobilisation de la connaissance », problématisé tel qu’articulé par le Conseil de recherches en sciences humaines du Canada. Les participants seront incités à se joindre à une discussion critique des avantages et des désavantages potentiels des exigences mandatées de la mobilisation des connaissances liée à la notion non précise de « bien public ».
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.151 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.058 | 0.218 |
| Scholarly communication | 0.073 | 0.053 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.031 | 0.040 |
| Insufficient payload (model declined to judge) | 0.005 | 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".