Education research and policy : steering the knowledge-based economy
Bibliographic record
Abstract
This volume considers the ways in which educational research is being shaped by policy across the globe. Policy effects on research are increasingly influential, as policies in and beyond education drive the formation of a knowledge-based economy by supporting increased international competitiveness through more effective, evidence-based interventions in schooling, education and training systems. What consequences does this increased steering have for research in education? How do transnational agencies make their influence felt on educational research? How do national systems and traditions of educational research - and relations with policy - respond to these new pressures? What effects does it have on the quality of research and on the freedom of researchers to pursue their own agendas? The 2006 volume of the World Yearbook of Education explores these issues, focusing on three key themes: * globalising policy and research in education * steering education research in national contexts * global-local politics of education research. The 2006 volume has a truly global reach, incorporating transnational policy perspectives from the OECD and the European Commission, alongside national cases from across the world in contrasting contexts that include North and South America, Canada, France, Singapore, China, Russia and New Zealand. The range of contributions reflect how pervasive these developments are, how much is new in this situation and to what extent evidence-based policy pressures on research in education build on past relationships between education and policy. This book considers the impact of the steering processes on the work and identities of individual researchers and considers how research can be organised to play a more active role in the politics of the knowledge economy and learning society.
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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 | 0.112 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.052 | 0.050 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".