Research and evidence in education decision-making: A comparison of results from two pan-Canadian studies
Bibliographic record
Abstract
In this paper we compare the use of research and other evidence in the policy formation practices of two groups of education policy elites, situated in different contexts – provincial education ministries and school districts. Data are derived from two pan-Canadian studies: Galway (2006) and Sheppard, Galway, Brown & Wiens (2013). The findings show that policy decisions at the ministry level are informed primarily by political and pragmatic factors, personal and professional beliefs and staff advice. The role of external research is shown to be relatively marginal and confined to quantitative studies and performance assessments. Decision makers at the school district level are less attendant to political and pragmatic influences relying more on personal beliefs, values and experiential factors supplemented by the advice of professional staff and in-house research/indicators. Results from both studies demonstrate limited reliance on external data and university-based research – the latter ranking 15th of 20 influencing factors. Consistent with Beck’s (1994; 1997) risk theory, we theorize that education policy making in both contexts is influenced by both macro- and micro-level factors, where choice of policy evidence is mediated by personal considerations and political risk factors. This suggests a weak policy development paradigm that is, to a large extent, resistant to independent research-informed evidence.
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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 | 0.045 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.036 | 0.069 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".