Exploring Understandings of “Good” Teaching: Disrupting Binaries and Embracing the In-Between
Bibliographic record
Abstract
The perceived boundary between educational theory and practice is never so pronounced as it is in initial teacher education. Pre-service teachers are apprenticed into a conversation system that seeks to flatten and homogenize practice rather than challenge status quo narratives of what it means to teach and learn. Emphasis on the techno-rational elements of teacher education - the how and what of teaching supersedes the dialogical, agentic, anti-oppressive potential of inhabiting and teaching in the space between binaried notions of theory and practice. Two recently completed studies of pre-service teachers and teacher education practitioners explore these phenomena to reveal that teaching exists between experiences that for pre-service teachers are both familiar and strange, and in the interstitial space - the gap - between factors that enable and constrain teacher educators in teaching for social justice in initial teacher education. Letting go of certainty, navigating between the familiar and the strange, the binaries of enable and constrain, acknowledges that tensions in education will always exist. Therefore, understandings of good teaching need to be open to a hermeneutic, anti-oppressive interpretation and analysis. This calls upon initial teacher education to build and foster purposeful dialogical in-between spaces to challenge the status quo.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.139 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".