How Schools Define Success: The Influence of Local Contexts on the Meaning of Success in Three Schools in Ontario, Canada
Bibliographic record
Abstract
Creating successful schools is a priority for governments, district officials, administrators, teachers and parents around the world, but just what does ‘school success’ mean? Grounded in theories of collective sense-making and learning, this article presents how school success is defined in three schools in Ontario, Canada, and draws on Ball, Maguire and Braun’s theory of policy enactment to explain similarities and differences between the schools’ definitions. A comparative case study of three elementary schools in the same neighbourhood finds that students’ happiness and academic learning (rather than achievement on standardized tests) are common aspects of each school’s multifaceted definition of success. Each school also has unique elements in its definition that can be attributed to differences in the schools’ situated, material, and professional contexts. In addition to local influences, class-based deficit ideology and professional discourses in their external contexts impact the schools’ definitions of success. Notably, the schools’ definitions emphasize individual growth and outcomes that reproduce rather than transform social inequities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".