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Record W7133167997

Secondary Education in Ontario: Effective Strategies for Prejudice Reduction Between Indigenous and Non-Indigenous Communities

2021· article· en· W7133167997 on OpenAlexaboutno aff
Jennifer Han

Bibliographic record

VenueTSpace · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPrejudice (legal term)CurriculumDismissalEconomic JusticeTraditional knowledgeIndigenous education
DOInot available

Abstract

fetched live from OpenAlex

This policy brief uses the lens of prejudice reduction to scrutinize Ontario’s secondary education curriculum in efforts to promote reconciliation between Indigenous and non-Indigenous communities in Canada. Ever since the founding of Canada, Indigenous peoples have experienced prejudice and discrimination in Canadian society. This paper argues that quality education promoting prejudice reduction is the most viable method to mitigate contemporary conflict and disputes between two communities such as the Coastal GasLink pipeline dispute and the Wet’suwet’en protest, residential school trauma, and overrepresentation of Indigenous peoples in the criminal justice system. Furthermore, it critically analyzes the current education system of Ontario to reveal the lack of availability of Native Studies, the dismissal of Indigenous values, and the recommendation of outdated textbooks that present the experiences of Indigenous peoples in a non-holistic narrative. The paper then insists on the implementation of school-based prejudice reduction strategies in the Ontario Curriculum that have been found effective in promoting peace in the Middle East through CISEPO and education programs for the Israeli-Palestinian conflict. Lastly, the paper recommends various teaching methods and updated resources to encourage holistic learning and understanding of Indigenous experiences in order to reduce prejudice and promote reconciliation between Indigenous and non- Indigenous communities.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.354
Teacher spread0.328 · 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 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
Published2021
Admission routes1
Has abstractyes

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