New practices, new methods, new voices
Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size AcknowledgementsWe would like to express our gratitude to Helen Nicholson, Joe Winston, Kathleen Gallagher and Colette Conroy for supporting us in editing this special issue. We would also like to thank all of the peer reviewers for their vital contributions throughout the busy academic year.Notes on contributorsDr Hannah Grainger Clemson undertakes and manages projects and research in applied arts/media, communities and education, with a particular interest in the multimodal construction of both local and international cultural narratives and identities. She contributes to the MA Drama in Education at the University of Warwick and has recently been working at the European Commission (Education and Culture) on schools' programmes and policies.Dr Burcu Yaman Ntelioglou is an assistant professor in the Faculty of Education at Brandon University, Canada. Her research and teaching focus on applied drama/theatre, the education of linguistically and culturally diverse students, language and literacy education, multiliteracies, multimodality, multilingualism and the use of collaborative, participatory and digital methodologies in research.Hannah Grainger ClemsonUniversity of Warwick, Coventry, UKBurcu Yaman NtelioglouBrandon University, Brandon, MB, Canada
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".