Bibliographic record
Abstract
This study examined factors at the school, district, and provincial level that influenced school administrators ’ responses to large-scale assessment. To understand administrators ’ perspectives, 5 secondary and 4 elementary administrators from a suburban school district in southern Ontario, Canada were interviewed. Key factors noted by administrators included school improvement planning (elementary versus secondary), departmental structures and teacher resistance within schools, lack of professional development opportunities, inadequate resources and direction from districts, ministry initiative overload, lack of an easily identifiable provincial assessment and evaluation policy document, and pressure to reach provincial achievement targets. The findings underscore the need for greater initial and ongoing professional development for school administrators as well as more thoughtful support from district leaders and provincial policy-makers. The development and implementation of accountability systems has been one of the most powerful, perhaps the most powerful, trend in educational policy in the last 20 years (Barber, 2004). In the 1980s, Prime Minister Margaret Thatcher’s adoption of a standardized test-based accountability system in Great Britain provided a model for
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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 | 0.035 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".