Bibliographic record
Abstract
Teacher educators often wonder about how best to prepare teachers for practice within a complex rather than a mechanistic system. As teacher educators, we facilitate a transformative inquiry (TI) course in which students investigate personally meaningful topics reflexively and relationally within larger educational and sociocultural contexts. This braided piece explores our own significant experiences with TI and how these experiences inform what we do as we mentor students through their own experience. By describing our personal entry points, we foreground some of the ways in which we work together to collaboratively and continuously revision the course. By making explicit our entry points into TI, we aim to reaffirm what matters to us as educators to improve our ability to deliberately engage in effective mentoring and to affirm our connections to the passions that sustain us amidst the many challenges and pressures that we face in our practice.
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 | 0.047 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.121 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.008 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".