Educational research in practice : making sense of methodology
Bibliographic record
Abstract
Part I: An Introduction to Educational Research in Practice 1. Why You Should Read This Book Joanna Swann, King's College London and John Pratt, University of East London 2. Making Sense of Methodology Joanna Swann and John Pratt Part II: Different Approaches to Educational Research 3. A Popperian Approach to Research on Learning Joanna Swann 4. The Post-modern Prism: Fracturing Certainty in Educational Research Elizabeth Atkinson, University of Sunderland 5. A Popperian Approach to Policy Research John Pratt 6. Critical Approaches to Research in Practice Michael Collins, University of Saskatchewan, Canada 7. Science in Educational Research William E. Tunmer, Jane E. Prochnow and James W. Chapman, Massey University, New Zealand 8. Research in a Bicultural Context the Case of Aotearoa/New Zealand Patricia Maringi G. Johnston, Te Whare Wananga o Awanuiarangi, New Zealand 9. Case-Study Research Michael Bassey, Nottingham Trent University Part III: Dialogues from a Research Community 10. Decision-making in the Real World: Post-modernism versus Fallibilist Realism Elizabeth Atkinson and Joanna Swann 11. Research and Social Improvement: Critical Theory and the Politics of Change Michael Collins and Joanna Swann 12. Culture, Race, and Discourse Patricia Maringi G. Johnston and John Pratt 13. How General are Generalizations? Michael Bassey and John Pratt Part IV: Doing Good Research 14. Principles for Good Practice John Pratt and Joanna Swann Glossary: Some Important Research Terms and their Usage John Pratt and Joanna Swann Index
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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 | 0.245 | 0.218 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.008 | 0.093 |
| Scholarly communication | 0.040 | 0.034 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".