Brokering knowledge mobilization networks: Policy reforms, partnerships, and teacher education
Bibliographic record
Abstract
Educational researchers and policy-makers are now expected by funding agencies and their institutions to innovate the multidirectional ways in which our production of knowledge can impact the classrooms of teachers (practitioners), while also integrating their experiential knowledge into the landscape of our research. In this article, we draw on the curriculum implementation literature to complicate our understandings of knowledge mobilization (KMb). Policy implementation, we suggest, can be understood as one specific type of KMb. We draw on different models for KMb and curriculum implementation and develop a relational model for KMb. Utilizing our model we critically reflect on the specific successes and challenges encountered while establishing, building, and sustaining the capacity of our KMb network. Our findings suggest that faculties of education are uniquely positioned to act as secondary brokers for the implementation of policy reforms within public education systems. To this end, we discuss how a relational KMb network is a “best practice” for establishing and sustaining partnerships among policy makers, educational researchers, and public school practitioners.
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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 | 0.040 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".