Bibliographic record
Abstract
The early years of the twenty-first century brought significant attempts across the United Kingdom (and elsewhere, for example Australia, Canada, Europe and the Nordic countries, Singapore, Taiwan and more recently the USA) to highlight the creativity in education by codifying it in the curriculum and emphasising the role of partnership work to inspire and nurture creativity in young people. Creativity and learning have increasingly been seen as intertwined, and with policy change has emerged a vocabulary around learning (and pedagogy) which frames ways in which educators and researchers understand how to develop both. Woods and Jeffrey (1996), Craft (1997) and Harland et al. (1998) during the 1990s had distinguished between creative teaching and teaching for creativity. Creative teaching has come to be understood as focused on exciting, innovative, engaging and often memorable pedagogy, whereas teaching for creativity was more overtly focused on the creativity of the learner. Creative learning, a term which emerged more through policy than research during the first decade of the twenty-first century, remains to great degree a term in search of meaning (Sefton-Green, 2008), though it has been described by Jeffrey and Craft (2006) as a ‘middle ground’ between creative teaching and teaching for creativity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".