Getting lost in translation? An analysis of the international engagement of practitioners and policy-makers with the educational effectiveness research base
Bibliographic record
Abstract
Educational effectiveness research (EER) has accumulated much knowledge in the areas of school effectiveness research (SER), teacher effectiveness research (TER) and school/system improvement research (SSIR). Yet many schools and educational systems are not making enough use of the material and their insights. The article reviews evidence of practitioner engagement and finds it limited in the areas of SER, greater in the area of TER and most prevalent in SSIR. Policy-maker engagement has been notable in some countries, but more limited in others. The article concludes by arguing for a new paradigm of EER that studies multiple levels of the educational system simultaneously utilising multiple methods and involves practitioners and policy-makers in a true EER community of expertise, in order to increase the reach and take-up of the discipline.
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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 | 0.373 | 0.520 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.027 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.036 | 0.029 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".