What needs to happen for school autonomy to be mobilised to create more equitable public schools and systems of education?
Bibliographic record
Abstract
The series of responses in this article were gathered as part of an online mini conference held in September 2021 that sought to explore different ideas and articulations of school autonomy reform across the world (Australia, Canada, England, Ireland, the USA, Norway, Sweden and New Zealand). It centred upon an important question: what needs to happen for school autonomy to be mobilised to create more equitable public schools and systems of education? There was consensus across the group that school autonomy reform creates further inequities at school and system levels when driven by the logics of marketisation, competition, economic efficiency and public accountability. Against the backdrop of these themes, the conference generated discussion and debate where provocations and points of agreement and disagreement about issues of social justice and the mobilisation of school autonomy reform were raised. As an important output of this discussion, we asked participants to write a short response to the guiding conference question. The following are these responses which range from philosophical considerations, systems and governance perspectives, national particularities and teacher and principal perspectives.
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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 | 0.040 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.020 |
| 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".