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Record W4391168197 · doi:10.4324/9781003263999

Promoting Inclusive Systems for Migrants in Education

2024· book· en· W4391168197 on OpenAlexaboutno aff
Paul Downes, Jim Anderson, Alireza Behtoui, Lore Van Praag

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceSociologyMathematics educationPsychology

Abstract

fetched live from OpenAlex

This novel contribution examines the lived experiences of migrants in education in various international contexts, exploring common school system features that promote students’ inclusion and challenge their exclusion. With a range of international contributions and case studies from Canada, the US, Hong Kong, Japan and Europe, the book offers critical, theoretically innovative understandings examining national policies and practices to develop reforms, focusing on agency, heterogeneity and systems of relational spaces for migrant youth. Chapters engage with discussions around differentiated needs of marginalised and vulnerable groups, as well as the importance of superdiversity in studying and developing inclusive systems for migrant youth in education. Offering unique insights, the book outlines a framework for the promotion of inclusive school systems that ultimately look to create quality learning environments that prevent discrimination, and support students’ holistic needs. It will be of great interest to researchers, academics and postgraduate students in the fields of sociology of education, philosophy of education, psychology of education, teacher education and social policy. Chapter 4 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution (CC-BY) 4.0 license. This work was supported by an Economic and Social Research Council [grant number ES/S015752/1].

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.510
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.399
Teacher spread0.365 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2024
Admission routes1
Has abstractyes

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