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Record W2188710921 · doi:10.82308/17143

Problematizing social studies curricula in Nova Scotia

2011· dissertation· en· W2188710921 on OpenAlexaboutno aff
Pamela Rogers

Bibliographic record

VenueeScholarship@McGill (McGill) · 2011
Typedissertation
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNova (rocket)CurriculumSociologyPolitical sciencePedagogyEthnologyEngineeringAeronautics

Abstract

fetched live from OpenAlex

This study questions the implementation of social studies curriculum in Nova Scotia. Indigenous knowledge, and anti-racism educational principles form the basis of high school curricula created from the perspective of African Nova Scotian and Mi'kmaq histories. The courses, African Canadian Studies and Mi'kmaq Studies, were implemented in 2002. To understand the distance between the intended objectives and practical realities of the curriculum, three methods were used: narrative, critical discourse analysis, and teacher interviews. Narrative provided a springboard for the analyses that follows by situating the context in the classroom. Centering on specific language use, critical discourse analysis connects implementation problems to the discourses employed in each curriculum document. The teacher interviews exposed the depth of issues through practical experiences, and critique of the school system, which link back to the knowledge which African Canadian Studies and Mi'kmaq Studies were formulated upon. The analysis connects the three methods to illustrate implementation issues in a broader context.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.150
GPT teacher head0.387
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2011
Admission routes1
Has abstractyes

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