Policy incentives in Canadian large-scale assessment: How policy levers influence teacher decisions about instructional change
Bibliographic record
Abstract
Large-scale assessment (LSA) is a tool used by education authorities for several purposes, including the promotion of teacher-based instructional change. In Canada, all 10 provinces engage in large-scale testing across several grade levels and subjects, and also have the common expectation that the results data will be used to improve instruction in classrooms. Yet despite agreement between ministries that instructional change based on LSA results is a positive development and employs data-based decision making at its heart, there remain significant differences in the kinds of incentives written into assessment policies in Canada. It is also true that implementation of the policies is less than uniform between schools and school divisions. Using mixed methods (survey data and follow-up interviews), this study examines which policy factors have the most significant impact on teacher decisions regarding the use of data. The findings indicate that highly incentivized policies correlate well to instructional change including aspects of both teaching (to) the curriculum as well as teaching to the test. Since the latter will be examined as a neither an educationally defensible practice nor a stated policy goal, the statement that ‘incentives work’ does not fully capture the nature of these impacts.
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 | 0.038 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".