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Building Welcoming and Inclusive Schools for Immigrant and Refugee Students: Policy, Framework and Promising Praxis

2019· book-chapter· en· W2950410990 on OpenAlexaboutno aff
Linyuan Guo-Brennan, Michael Guo-Brennan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeImmigrationPraxisPedagogyEthnic groupPolitical scienceCultural diversityDiversity (politics)Sociology

Abstract

fetched live from OpenAlex

Abstract In 2017, 22% of the Canadian population are foreign-born immigrants and one in five is a visible racial minority. Canadian schools and classrooms mirror the diversity of the society and are populated with more and more immigrant and refugee students from diverse ethnic, cultural and linguistic backgrounds each year. Uprooted from their home countries and familiar environments, immigrant and refugee students experience barriers and challenges in new living and educational environments. The increasing number of immigrant and refugee students and their unique educational needs and challenges have called building welcoming and inclusive schools a priority in Canadian education system. This chapter addresses the urgent need for high-impact policies, practices and praxis to build welcoming and inclusive schools for immigrant and refugee students through cross-sector community engagement. Based on several empirical studies, critical and extensive literature review and authors’ professional reflections, this chapter introduces a theoretical framework of building welcoming and inclusive schools for immigrant and refugee students and introduces the promising strategies of engaging community stakeholders, including educators, students, parents, governments and community organizations and agencies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.014
GPT teacher head0.373
Teacher spread0.359 · 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 designTheoretical or conceptual
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

Citations33
Published2019
Admission routes1
Has abstractyes

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