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Record W2073212625 · doi:10.1080/13603116.2014.964573

Conceptualising diversity in a rural school

2014· article· en· W2073212625 on OpenAlexaffabout
Stephanie Tuters

Bibliographic record

VenueInternational Journal of Inclusive Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiversity (politics)PovertyRural areaSociologyMeaning (existential)RuralityQualitative researchPopulationPedagogyInclusion (mineral)Focus groupEconomic growthPolitical sciencePsychologySocial scienceDemography

Abstract

fetched live from OpenAlex

This article describes a qualitative study which investigated how teachers made meaning of and responded to diversity in their rural school. While there is a large amount of information regarding how diversity plays out in urban settings and how teachers respond to it [e.g. Dei, G. J. S., I. M. James, L. L. Karumanchery, S. James-Wilson, and J. Zine. 2003. Removing the Margins: The Challenges and Possibilities of Inclusive Schooling. Toronto: Canadian Scholars' Press], little exists regarding rural schools. This is particularly troubling because of the large proportion of students attending rural schools. Data for this study were collected during individual interviews with seven elementary school teachers in a rural school in Ontario, Canada. Participants highlighted a number of categories of difference amongst their student cohort and how the challenges associated with this diversity were compounded by living in a rural area. The perceptions of participants are mirrored in educational policy and literature. Rural areas are expanding in population and diversity, and rural students are experiencing poverty and educational failure at the same levels of many large urban centres [Barlow, D. 2008. "America's Forgotten Schools." The Education Digest 22 (8): 67–70]. Yet rural schools are being ignored in educational policy, largely based on misconceptions about the nature and value of rural environments.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.347
Teacher spread0.333 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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