Bibliographic record
Abstract
The post-modern curriculum, drawing on chaos and complexity theory, recognises the realities of a world in flux and posits that the teacher and the class are always teetering in the midst of chaos, ‘not linked by chains of causality but [by] layers of meaning, recursive dynamics, non-linear effects and chance’ (Osberg 2008, viii). The phenomenon of such (paradoxically) ‘constant’ change is a living part of the realisation of Everyday Theatre company performances that are, in turn, specifically designed to effect shifts or changes in understanding for their audience participants. It is this changing, changeful, changeable, change-ringing context that we investigate in this article as we look at the layering of the experiences of Everyday Theatre through the lens of a post-modern curriculum of complexity. For us, the work of Everyday Theatre exemplifies the post-modern curriculum at its best, confirming what we as drama educators have intuited: a rich pedagogy unfolds in the midst of chaos as students meet a complex narrative that creates powerful metaphors they are invited to explore through their own acts of experience.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".