Multistakeholder Networks and Evidence-Informed Practice in Education: A Case from Ontario
Bibliographic record
Abstract
Abstract In this chapter, we discuss the case of the Knowledge Network for Applied Education Research (KNAER) to illustrate the context for evidence-informed practice (EIP) in Ontario, Canada. KNAER (www.knaer-recrae.ca) is an initiative to strengthen relationships between research producers, users, and the communities that schools serve to improve outcomes for students in four priority areas: mathematics, equity, well-being, and Indigenous knowledge. As developmental evaluators for KNAER from 2017 to 2019, we reference and integrate two main sources of data: a research model created to inform the network's planning and activities, and semistructured interviews with network leaders (N = 11) and policymakers (N = 3). Reflecting on our findings, we discuss five key lessons for EIP: the need to build reciprocal streets of engagement, the need to shift data use from accountability and compliance to partnership learning, the need to coproduce and identify specific entry points of change, the need to focus on capacity building and leveraging brokers across partnerships, and the need to use communication as a problem-solving tool to assess and adjust innovations and implementation rather than passive reports of activities.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".