Looking Forward: Honouring the Past and Changing the Present to Create our Future
Bibliographic record
Abstract
This chapter considers what we have learned from the book and identifies some key issues that might need to be considered in the future. It starts by providing the author's personal history of how teacher education has changed over the time that ICET has been in existence, with a focus on its shift from a craft-based activity to a research-led profession, identifying some of the issues that arose during this journey. It uses the example of standards for school leadership and associated training programs as a way of explaining how different parts of the world judge excellence and how Neo-Liberal Public Management policies have changed the lives of teachers, school leaders and teacher educators as well. The chapter argues that such policies try to simplify what is a very complex process and in doing so have created a situation where fewer high school graduates want to become teachers and even fewer teachers wish to lead schools. The Scottish and Canadian examples are used as a means of demonstrating attempts to improve teacher education using collaboration rather than accountability measures and flags the possibility that there might be common elements of teacher education preparation that go beyond both time and location and that future exploration of a global approach might be something that ICET is well positioned to do in the future.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".