Bibliographic record
Abstract
Contemporary discussions about curricula, whether for schools or for higher education, come with demands for change to cope with uncertainty, unpredictability, and 21st century challenge. Core notions however of curriculum as structure for providing access to knowledge (Young, 2014), or a set of teaching and learning prescriptions (Scott, 2014) are hard to shift, yet questions of what knowledge and how is it formed are ones which should be critical for teacher education, where, as a site of higher education, decisions about that knowledge are contested (Barnett, 2015). The prospective teacher continues to negotiate tensions between knowledge of content and of curriculum, pedagogical knowledge and generic teaching skill, knowing as process and knowing in performative terms, and between shaping a role as teacher and as educator for the 21st century. This paper makes a case for the potential that an arts oriented course, specifically drama, holds for helping student teachers to conceptualise their own learning experiences within a teacher education curriculum, to better understand the facilitation of learning experiences within school and classroom curricula, and to begin to shape their teacher identity.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".