Scarcely visible? Analysing initial teacher education research and the Research Excellence Framework
Bibliographic record
Abstract
In the UK, the Research Excellence Framework is a mechanism used for ranking the quality of research in higher education institutions. While there has been analysis of the entire Research Excellence Framework, and of the Education unit of assessment more generally, analysis of how research on initial teacher education featured in the Research Excellence Framework has been minimal. In this article, we report on Phase I of an 18-month project that mapped the extent to which initial teacher education-focused research was included in the 2014 Research Excellence Framework. Employing a novel methodology and a theoretical framework based on policy as text and discourse, we identify a sample of 12 higher education institutions that provided initial teacher education programmes and returned outputs to the 2014 Research Excellence Framework. Analysis of over 1,600 outputs suggest that in the 2014 Research Excellence Framework only 5.5 per cent of these were focused on initial teacher education. We discuss the methodological approach, some headline findings and areas for future research, arguing that these add evidence to the literature of initial teacher education-focused research and, in doing so, can inform policy at the levels of schools, higher education institutions, Research Excellence Framework and the government. We conclude that although the Research Excellence Framework only concerns the UK, similar exercises are becoming increasingly prevalent globally, and therefore the extent to which research on initial teacher education was marginalised in the 2014 Research Excellence Framework is of interest to all concerned with teacher education.
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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 | 0.164 | 0.335 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".