Bibliographic record
Abstract
1. Introduction: Evaluating Creativity Julian Sefton-Green, Weekend Arts College 2. Art Education and Talk: From Modernist Silence to Postmodern Chatter Karen Ramey, University of East London and Howard Hollands, Middlesex University 3. Evaluation and Design and Technology John Garvey, Brunel University School of Education and Anthony Quinlan, Sudbury Junior School, Wembley 4. Writing in English and Responding to Writing Muriel Robinson, University of Brighton and Viv Ellis, University of Brighton 5. Music as a Media Art: Evaluation and Assessment in the Contemporary Classroom Lucy Green, London University Institute of Education 6. Measuring the Shadow or Knowing the Bird: Evaluation and Assessment in Drama Education John Somers, Exeter University 7. Making the Grade: Evaluating Student Production in Media Studies David Buckingham, Institute of Education University of London, Pete Fraser, Long Road Sixth Form College, Cambridge and Julian Sefton-Green, Weekend Arts College 8. Whose Art is it Anyway? Art Education outside the Classroom Rebecca Sinker, Middlesex University 9. Making Multimedia: Evaluating Young People's Creative Multimedia Production Rebecca Sinker, Middlesex University 10. From Creativity to Cultural Production: Shared Perspectives Julian Sefton-Green, Weekend Arts College
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.017 |
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".