Brokering knowledge mobilization networks: Policy reforms, partnerships, and teacher education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it