MétaCan
Menu
Back to cohort
Record W3156567321 · doi:10.52547/johepal.2.1.45

Culturally Responsive Pedagogy: A Canadian Perspective

2021· article· en· W3156567321 on OpenAlexafffundabout
Stephanie Chitpin, Olfa Karoui

Bibliographic record

VenueJournal of Higher Education Policy And Leadership Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Saskatchewan
KeywordsStandardized testPedagogyRepresentativeness heuristicChristian ministrySociocultural perspectivePerspective (graphical)Cultural diversityDiversity (politics)Cultural competenceCompetence (human resources)Sociocultural evolutionPsychologySociologyMedical educationPolitical scienceMathematics educationSocial psychologyMedicine

Abstract

fetched live from OpenAlex

In the Canadian province of Saskatchewan, the Ministry of Education has encouraged sociocultural and linguistic diversity within schools.Yet, as is typical within neoliberal societies, standardized assessments continue to be promoted for evaluating student progress and for the validity of in-school assessments.Standardized testing is designed using a monolingual Eurocentric perspective which ultimately discounts many cognitive processes used by non-Canadian EAL students.This study describes ways in which educational leaders work towards incorporating culturally responsive pedagogy into their practices so as to increase representativeness within their schools.Six principals in rural Saskatchewan were interviewed.Results revealed three common themes: the use of standardized data to assess student progress and in-school testing, integration of cultural competence practices, and initiation of individualized intervention strategies.

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)0.007
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 score0.193
Threshold uncertainty score0.936

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0300.022
Scholarly communication0.0110.003
Open science0.0030.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.246
GPT teacher head0.497
Teacher spread0.251 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Higher Education Policy And Leadership StudiesSame topicEducation Systems and PolicyFrench-language works237,207