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Record W2163383772 · doi:10.1080/00220270600968658

Student experiences of a culturally‐sensitive curriculum: ethnic identity development amid conflicting stories to live by

2006· article· en· W2163383772 on OpenAlexaboutno aff
Elaine Chan

Bibliographic record

VenueJournal of Curriculum Studies · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumEthnic groupPedagogyContext (archaeology)Identity (music)NarrativeSociologyCurriculum developmentAnthropology

Abstract

fetched live from OpenAlex

This study examines ways in which students’ experiences of a culturally‐sensitive curriculum may contribute to their developing sense of ethnic identity. It uses a narrative‐inquiry approach to explore students’ experiences of the interaction of culture and curriculum in a Canadian inner‐city, middle‐school context. It considers ways in which the curriculum may be interpreted as the intersection of the students’ home and school cultures. Teachers, administrators, and other members of the school community made efforts to be accepting of the diverse ethnic, linguistic, and religious backgrounds that students brought to the school. However, examination of students’ experiences of school curriculum events and activities revealed ways in which balancing affiliation to their home cultures while at the same time abiding by expectations of their teachers and peers in their school context could be difficult. The stories highlight ways in which curriculum activities and events may contribute to shaping the ethnic identity of students in ways not anticipated by teachers, administrators, and policy‐makers.

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)0.007
metaresearch head score (Gemma)0.010
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0210.017
Scholarly communication0.0100.006
Open science0.0020.014
Research integrity0.0030.007
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 head0.064
GPT teacher head0.491
Teacher spread0.427 · 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

Citations67
Published2006
Admission routes1
Has abstractyes

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