Reorienting toward complexity in teacher education
Bibliographic record
Abstract
We are teacher educators committed to reorienting toward complexity in teacher education, given the multiplex terrain of the education landscape that awaits teacher candidates (TCs) upon receiving their Bachelor of Education degree. Within our context, we are preparing teachers to work with a recently revised curriculum and many regional, national, and global challenges and mandates, especially the Calls to Action of the Truth and Reconciliation Report of Canada (2015). By encouraging the agency of the TCs – the scholar-practitioners – with whom we work, we aspire to a reflective and deliberative approach of engaging with issues, needs, and problems as caring humans. This paper traces our journey of collaborative inquiry as we revisit, reframe and repurpose influential scholarship. This involves pedagogical conceptions of experience and reflective thinking, informing the mindful complexity of being and becoming in place, while foregrounding local Indigenous Ways of Knowing and experiential learning as illustrations of a scholar-practitioner stance. Through inquiry in community, recognising the importance of drawing upon individual Teacher Candidate identity, we articulate how we learn as teacher educators to address the complex, contemporary issues of: equity, diversity, inclusion; decolonisation; education in times of crisis; and the challenges of ecological well-being.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.080 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".