Evidence-Based Policy Development for Literacy and Numeracy
Bibliographic record
Abstract
2 The title of this presentation is Evidence-Based Policy Development for Literacy and Numeracy and while the focus of this conference is how, through policy and practice we can improve literacy and numeracy levels across Canadian provinces, the question of policymakers’ use of research-based evidence is broader than literacy and numeracy. So I suppose I could be giving this paper at a similar conference on special education or any other area of education, for that matter. The paper will not, therefore focus exclusively on literacy and numeracy, but on the role of evidence in education policy development generally. This paper is organized in three parts. First, I want to give you some background on how the Canadian educational policy context has changed and describe why I was interested in conducting research on the question of “what counts as evidence for education policy making”. In the second part of the presentation I’ll share some of the findings from this research. Finally, I’ll discuss how I think we can understand education policy making as it exists today and suggest ways in which it could be different.
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 | 0.128 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".