Bibliographic record
Abstract
<JATS1:p>What do teachers learn ‘on the job’? And how, if at all, do they learn from ‘experience’?</JATS1:p> <JATS1:p>Leading researchers from the UK, Europe, the USA and Canada offer international, research-based perspectives on a central problem in policy-making and professional practice – the role that experience plays in learning to teach in schools. Experience is often weakly conceptualized in both policy and research, sometimes simply used as a proxy for ‘time’, in weeks and years, spent in a school classroom. The conceptualization of experience in a range of educational research traditions lies at the heart of this book, exemplified in a variety of empirical and theoretical studies. Distinctive perspectives to inform these studies include sociocultural psychology, the philosophy of education, school effectiveness, the sociology of education, critical pedagogy, activism and action research. However, no one theoretical perspective can claim privileged insight into what and how teachers learn from experience; rather, this is a matter for a truly educational investigation, one that is both close to practice and seeks to develop theory.</JATS1:p> <JATS1:p>At a time when policy-makers in many countries seek to make teacher education an entirely school-based activity, Learning Teaching from Experience offers an essential examination of the evidence-base, the traditions of inquiry – and the limits of those inquiries.</JATS1:p>
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".