Learning, for a Change: School Improvement as Capacity Building
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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 it