Educating Teachers and Fostering Authentic Professional Learning in an Era of Austerity, Global Competition and Quality Assurance Rhetoric
Bibliographic record
Abstract
Writing from the perspective of both an instructor in a teacher education program at University of Toronto and more importantly as a mentor for teacher candidates in the classroom, hosting for over twenty years student teachers from six universities in Ontario and New York, the paper explores the master-apprentice relationship within the practicum placement in schools - drawing philosophically on Martin Heidegger's reflections on apprenticeship, Donald Schön's pragmatic emphasis on studio work and Lee Shulman's focus for training on developing subject related pedagogical-content-knowledge, to resituate the significance of what many educators and student-teachers say forms the core of teacher education. Subtle changes in teacher education over the last thirty years, set against dominant themes of professional autonomy and agency within sweeping educational and economic reforms such as the neo-liberal accountability and austerity movements, are sketched in order to follow their arc or trajectory into possible futures. Using a Foucauldian genealogical approach, the author aims to show how we could think and act differently in our practices and governance of teacher education.
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 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".