Investigating the challenges and opportunities of a bilingual equity knowledge brokering network: A critical and reflective perspective from university partners
Bibliographic record
Abstract
Public education systems are often large, diverse, fragmented, and historically very hard to change. While previous reforms targeted primarily school staff, large-scale policies now include a broader audience including non-system organizations (e.g., knowledge brokering organizations) that may influence directly or indirectly policy implementation. Arguing that knowledge brokering organizations can contribute to policy implementation by bridging equity policy and research and practice at the local level, we put forward that their networks and relationships with districts, schools, and community organizations can bring about substantial changes to the organization and practices of schools on equity issues, even though they may face obstacles in implementing change due to particular contexts. We aim to better understand the role of knowledge brokering networks and of the university partners who act as knowledge brokers to bridge Ontario Ministry of Education policy goals with equity research and practice. As knowledge brokers working in a bilingual province-wide equity knowledge brokering network, we use our experiences as a particular case of a non-system role in system-wide reforms. We build on these experiences to question and self-reflect on our role as knowledge brokers who accompanied practitioners and community coalition leaders towards equity and inclusion over a 2-year period. By analyzing knowledge brokering functions, we show the challenges and opportunities we faced as knowledge brokers in guiding local equity and inclusion initiatives: 1) the roles we carried out during interactions and practices that could take on different meanings as knowledge producers and mobilizers; 2) we point out how and why these knowledge brokering functions and our roles within a bilingual province-wide network needed to adapt to local realities by providing for a more flexible planning process that allowed for sufficient time to identify local needs and to produce, if necessary, the knowledge that incorporated cultural context considerations or particularities.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".