A developmental evaluation of research-practice-partnerships and their impacts
Bibliographic record
Abstract
Globally, K-12 education systems are grappling with how best to integrate research and evidence into policy and practice. Research-practice-partnerships (RPPs) have arisen as a potentially powerful mechanism for school improvement. This study investigates the ways four research-practice-policy-partnerships are addressing impact by (a) reporting on metrics being used to assess brokering and partnerships, and (b) exploring the ways that network leads and policymakers conceptualize partnerships and impact on the frontlines. Our findings suggest that while metrics being used provide a necessary baseline for number and types of partnerships, more robust methods are needed capture the quality of interactions and to strategically inform network development. Network leads conceptualize impact in relation to collaborative processes (shared goals; new and diverse partnerships; improved student achievement; system alignment); systems and structures (joint-work; funding and sustainability; demand from practitioners; equity); continuous learning (capacity-building; reach; adaptability; storytelling). Our discussion provides ideas about network improvement that include sharing cases of failures (alongside exemplary cases) to maximize learning, and advocates for the use of developmental evaluation to explore the impacts of RPPs.
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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 | 0.346 | 0.469 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".