5th presentation Warren Linds’s story, “Masks in metaxis”
Bibliographic record
Abstract
Runner: Our last presenter in this session on drama work in dissertations is Dr. Warren Linds. He was an instructor in the Faculty of Education, University of Regina and in the Saskatchewan Urban Native Teacher Education Program. He is currently an Assistant Professor in Applied Human Sciences at Concordia University. His doctorate is another one from the University of British Columbia, which seems to be, along with Fielding Graduate University, the source of many of our most creative dissertations being represented here. His interests are in the facilitation and development of transformative drama processes through a performative writing and research methodology. He is also interested in the exploration of drama as an anti-racist pedagogy and how embodied and reflective experiences of improvisation in teaching are developed. Among other publications, he is a co-editor of the 2001 text, Unfolding Bodymind: Exploring Possibility Through Education. Warren.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".