Transformative schooling: a polyphonic study of the possibilities for progressive education in Nova Scotia
Bibliographic record
Abstract
The purpose of this thesis is to discover the possibilities for implementing progressive ideologies and a just education in the Nova Scotia public school system. I used a mixed qualitative approach to conduct the research. The use of the term 'Polyphonic', meaning many sounds, implies the use of multiple voices in this work. In-depth interviews were conducted with public school teachers, principals and teacher educators, a focus group was conducted with nine students from an alternative public high school, two classroom observations were done and document interpretation was used, creating a rich and detailed qualitative study. The data was analyzed using a Freirian educational theory based on ideas of critical pedagogy and transformative education. Liberal and progressive ideologies provide the foundation for an education promoting social and global justice. Though the province has begun to recognize the need for progressive schooling, many official policies and practices work in opposition to this goal. Individual teachers are implanting progressive practices in the classroom but this is only individual-level change. Large scale reform requires implementing progressive ideas at the system level. As long as the province continues to work both for and against this need, a system level, progressive education, promoting global justice, can never be established.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".