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Record W7079463804 · doi:10.26108/9p3h-mw10

Transformative schooling: a polyphonic study of the possibilities for progressive education in Nova Scotia

2007· article· en· W7079463804 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2007
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningIdeologyProgressive educationReflexivityAdult educationQualitative researchFocus groupOpposition (politics)Interpretation (philosophy)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.292
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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