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Record W2932781581

Teaching Science in Cultural Diversity-rich Classrooms: Teachers’ Encountering Religious Oppositions

2018· article· en· W2932781581 on OpenAlexaffabout
Latika Raisinghani

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAcknowledgementPedagogyDiversity (politics)SociologyMulticulturalismCultural diversityMulticultural educationScience educationTeaching methodTransformational leadershipTeacher educationMathematics educationPsychologySocial psychologyAnthropology
DOInot available

Abstract

fetched live from OpenAlex

This paper reports K-12 teachers’ perspectives about their experiences of facing religious resistance while teaching science teaching in their elementary and secondary classrooms in a large urban city in Western Canada. The study stems from a larger doctoral research which drew upon the principles of a qualitative case study approach and employed phenomenographic methods to investigate teachers’ perspectives. Data corpus was analyzed and interpreted by using a comprehensive (trans-multi)culturally responsive education framework which amalgamates transformational and critical multicultural education perspectives and notions of culturally responsive teaching. Key findings include: 1) Teachers’ experiences of encountering religious restrictions in their science classrooms 2) Teachers’ acknowledgement of teaching science as an uncontroversial knowledge. These findings illustrate the complexities involved in teaching science to culturally diverse students and highlight the need for promoting (trans-multi)culturally responsive understandings among teachers and creating spaces for increased teacher-parental collaboration to facilitate learning of science in diversity-rich classrooms of Canada.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
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 head0.043
GPT teacher head0.342
Teacher spread0.299 · 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.

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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicReligious Education and SchoolsFrench-language works237,207