Absented narratives on racism and white social framing: critical social inclusion in Finnish and Canadian integration education programs
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.064 | 0.036 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".