And thereby hangs a tale ... : Narrative research on learning: comparative and international perspectives. Sheila Trahar (ed.), with a foreword by Ruth Hayhoe [book review]
Bibliographic record
Abstract
Book review of: Narrative Research on Learning: comparative and international perspectives / Sheila Trahar (ed.), with a foreword by Ruth Hayhoe ; Symposium Books, Oxford, isbn: 1873927606". Once upon a time, a group of academics, teachers and independent consultants got together at Bristol University in the United Kingdom, to talk about the power of stories in teaching. Though the group was made up of people from many different countries and cultures, it was clear they shared a belief in the universality of narrative as a facilitator of learning. To demonstrate this conviction, 17 members of the group put their views into a collection of reflections, theoretical arguments, pedagogical investigations and academic opinions, edited together by Sheila Trahar, herself a committed practitioner researcher from the University of Bristol. With a foreword by Ruth Hayhoe, a Professor of education based at the Ontario Institute for Education, the resulting compilation of essays and case studies offers a series of practical insights designed to illuminate the potentiality of narrative as a tool in the field of international pedagogy.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".