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Record W4415546545 · doi:10.1080/13603116.2025.2579729

Absented narratives on racism and white social framing: critical social inclusion in Finnish and Canadian integration education programs

2025· article· en· W4415546545 on OpenAlexaboutno aff
Tobias Pötzsch

Bibliographic record

VenueInternational Journal of Inclusive Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsRacismNarrativeInclusion (mineral)White (mutation)Anti-racismSocial integrationRacial biasCritical theoryNarrative inquiry

Abstract

fetched live from OpenAlex

This article explores how critical social inclusion is contested within integration education programmes for adult migrants, by focusing on narratives which are ubiquitous, yet seemingly unmentioned in shaping inclusion efforts. Drawing on a theoretical foundation derived from anti-oppressive education as well as critical migration & critical whiteness studies it focuses on two such absented discourses, namely Racism's Omission and White Social Framing. Their omission exposes Inclusections, – the intersections of inclusion and exclusion – that position migrants enrolled in SFI (Swedish for Immigrants) and LINC (Language Instruction for Newcomers to Canada) programmes in the liminal spaces between belonging and othering. Based on multiple case study fieldwork drawing upon data from interviews and participant observations with staff and students in SFI programs Helsingfors and Mariehamn, Finland and a LINC program in Edmonton, Canada, between 2016-2018, the findings illustrate that topics of racism and whiteness only surfaced when they intersected practices or bodies that could not be embraced by dominant civic integrationist narratives. The inclusectional outcomes of such discursive silences were that they could circumscribe or invalidate migrant experiences while simultaneously opening up avenues of resistance for teachers and students.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
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 head0.020
GPT teacher head0.449
Teacher spread0.429 · 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 designOther design
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
Published2025
Admission routes1
Has abstractyes

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