“Putting Science into Action”: A Case Study of How an Educational Intermediary Organization Synthesizes and Translates Research Evidence for Practice
Bibliographic record
Abstract
Background: Knowledge brokering by intermediary organizations includes knowledge distillation processes (e.g., synthesis, translation).Aims and objectives: This article explores how an educational intermediary performs research distillation when creating virtual knowledge reservoirs for educators.Methods: The authors use qualitative data from semi-structured interviews, coded to consensus, and thematically analyzed.Findings: During synthesis, intermediaries apply a “research lens” to evaluate the credibility of the evidence. During translation, they rely on their experience as educators to share evidence in a non-academic voice and generate “turnkey” strategies.Discussion and conclusion: The article considers the knowledge brokering pipeline fallacy and examines the potential of ongoing processes for improving the effectiveness of intermediaries with the aim of diminishing research-practice and research-policy gaps in education.
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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 | 0.048 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.030 | 0.022 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".