Improving Research-Policy Relationships: Lessons from the Case of Literacy Paper prepared for the OISE/UT International Literacy Conference: Literacy Policies for the Schools We Need Toronto
Bibliographic record
Abstract
The relationship between research and policy, a long-standing concern in education, has taken on even greater salience in recent years. Researchers feel that their knowledge is not given sufficient weight in policy or practice while policy-makers feel that they cannot get timely assistance with the questions of importance to them. The picture is not as bad as often claimed; in fact, research has had strong impacts on policy in education over time. A main barrier to greater impact is the reality that research and policy are different contexts for knowledge production and use, each producing its own incentives, constraints and pressures. Stronger links between research and policy are possible if there is greater understanding of the realities of each context and the links that can exist between them. Politics and policy-making are not well understood by those who are not directly involved, so this paper focuses largely on the nature of government and policymaking, and how research might influence that process more effectively with specific reference to issues of early literacy.
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 | 0.145 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.035 | 0.064 |
| Scholarly communication | 0.044 | 0.045 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.029 | 0.024 |
| 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".