Introduction: Knowledge Mobilization - The new research imperative
Bibliographic record
Abstract
Hovir can educational research have more impact~ How do we know the depth and scope of the impact it has~ \\Vhat processes of knowledge exchange are most effective for increasing the uses of research results? How can researchproduced knmvlcdge be better 'mobilized' among users such as practising educators, policy-makers and the public communities? These sorts of questions, despite their many embedded definitional, philosophical and pragmatic problems, arc commanding urgent attention in educational discourses and research policies no\\\\r circulating in the UK and Europe, Canada and the USA and Australia and other parts of the world. This attention has been translated into powerful material exercises that shape \\vhat is considered to be worthv·,lhile research and hmv research is funded, recognized and assessed. Granting agencies request knmvledge mobilization or knovdedge exchange plans and otTer special funds for these purposes. Researchers and universities arc explicidy directed, in research design and accountability, to emphasize knowledge exchange or mobilization - announced by one funding council as a core priority (SSHRC 2008, 2010).
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".