Bibliographic record
Abstract
The characteristics of improving schools have been widely documented and disputed within the international research field of school improvement (Hopkins et al, 1994; Stoll and Fink, 1996; Harris, 1999). Successive studies have shown that there are a number of factors that support positive school change. These include purposeful leadership, teacher collaboration and a central focus upon learning outcomes ( Fullan , 1992). Yet, despite a good deal of research describing schools once they have improved, there is surprisingly little known about how they get there. The existing literature provides a range of descriptions of different types of school improvement projects. There are however, relatively few detailed studies of successful school improvement projects in action and even fewer studies of a comparative nature. This article considers two school improvement projects that have been shown to have a positive effect upon teaching and learning outcomes. The Improving the Quality of All Project (IQEA) in the United Kingdom and the Manitoba School Improvement Project (MSIP) in Canada have both demonstrated considerable success in their work with schools (Earl and Lee, 1998; Hopkins and Harris, 1997). These projects are well known within the international research community and provide a basis for comparing ewhat works' in different countries and in contrasting educational contexts.
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.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".