De/colonizing Preservice Teacher Education: Theatre of the Academic Absurd
Bibliographic record
Abstract
Where does the work of de/colonizing preservice teacher education begin? Aboriginal children‘s literature? Storytelling and theatrical performance? Or, with a paradigm shift? This article takes up some of these questions and challenges, old and new, and begins to problematize these deeper layers. In this article, the authors explore the conversations and counterpoints that came about looking at the theme of social justice through the lens of First Nations, Metis, and Inuit (FNMI) children‘s literature. As the scope of this lens widened, it became more evident to the authors that there are several filters that can be applied to the work of de/colonizing preservice teacher education programs and the larger educational system. This article also explores what it means to act and perform notions of de/colonization, and is constructed like a script, thus bridging the voices of academia, theatre, and Indigenous knowledge. In the first half (the academic script) the authors work through the messy and tangled web of de/colonization, while the second half (the actors‘ script) examines these frameworks and narratives through the actor‘s voice. The article calls into question the notions of performing inquiry and deconstructing narrative.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.041 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".