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Record W4255942247 · doi:10.4324/9781410603753-13

Multiculturalism, Diversity, and Education in the Canadian Context: The Search for an Inclusive Pedagogy

2001· book-chapter· en· W4255942247 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismDiversity (politics)Context (archaeology)PedagogySociologyPolitical scienceGeographyAnthropology

Abstract

fetched live from OpenAlex

The Canadian Multicultural Policy, introduced by the federal government in 1971, established a framework that, over the years, has informed the ways in which educational institutions have come to recognize the cultural diversity of the Canadian population, and has initiated educational programs that address issues related to race, national and ethnic origin, color, and religion. Although some educators and writers claim that multicultural education as implemented in schools has been responsive to the needs, interests, and aspirations of the diverse population of students (Mansfield & Kehoe, 1994; Samuda & Kong, 1986), critics have argued that this approach to education has been limited in its capacity to do so particularly with regard to marginalized students. To support their point, the critics pointed to the situation where, even with the implementation of “multicultural education,” low teacher expectations continue to contribute to the streaming of minority and immigrant students into low-level educational programs, resulting in alienation and high dropout rates (Cummins, 1997; Curtis, Livingstone, & Smaller, 1992; Dei, Muzza, Mclsaac, & Zine, 1998; Lucas & Schecter, 1992). On this basis, multicultural education as practiced has been unable to ensure equality of educational opportunities and, still less, equity for all students within the Canadian education system.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.116
Threshold uncertainty score0.841

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0240.025
Scholarly communication0.0140.005
Open science0.0020.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.000

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 head0.066
GPT teacher head0.392
Teacher spread0.326 · 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 designNot applicable
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

Citations20
Published2001
Admission routes1
Has abstractno

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Same topicCollaborative Teaching and InclusionFrench-language works237,207