Learning, for a Change: School Improvement as Capacity Building
Bibliographic record
Abstract
I know of no other strategy which has taken 20 or more schools and shown this level of success — even more quickly than we thought possible — and in a cost effi cient way. (M. Fullan) Since 1991, a number of secondary schools in the Canadian province of Manitoba have been part of an experiment in school improvement. The result? Many of these secondary schools have really moved — they have shown gains in student achievement and have become the kinds of schools that are likely to sustain improvement. Of the 22 schools that were involved in MSIP at the time of our intensive evaluation, one third showed substantial improve ment and half had made considerable movement along the continuum. Although the improvement in schools was excit ing, as evaluators we were particularly interested around how the schools 'got there' and whether or not they could sustain their movement. In our evaluation, we set out to look inside the 'black box'to describe how specific schools actually went about changing their schools, in practice with the hope of uncovering some general principles that could guide schools as the approach any improvement initiatives.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| 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".