Shaping Occupational Possibilities for Norwegian Immigrant Children: A Critical Discourse Analysis
Bibliographic record
Abstract
Elementary school education is a key occupational arena for the integration of immigrant children. In this study conducted in Norway, questions about how best to support the education of immigrant children arose partly due to their poorer performance in primary school testing. A critical discourse analysis of the construction of the problem of the educational gap between Norwegian and immigrant children was conducted drawing on a sample of 20 newspaper articles published in 2012 about educational matters in Oslo. The analysis deconstructed how issues related to immigrant children's performance were problematized, with particular foci on how occupation was drawn into solution frames and the occupational possibilities promoted for immigrant children and their families. Three problematizations of the educational gap were identified, with each locating the problem in a different rationale, specifically, linguistic deficiency, parental deficiency and spatial segregation. Within each problematization, although contrasting political rationalities emphasized individual or social solutions, occupations forwarded as means to address the gap and promote integration were narrowly defined in ways that focused on assimilation into Norwegian ways of doing and de-valued difference. Concerns are raised regarding the implications of this narrow framing of occupational possibilities for identity, well-being, and occupational marginalization of immigrant children and families.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".