Assimilering eller mångkulturell inkorporering? Nyanlända elever i den svenska och kanadensiska skolan
Bibliographic record
Abstract
Artikeln tar sin utgångpunkt i en jämförelse mellan svensk och kanadensisk skolpolitik och hur respektive land arbetar med frågor kring integration och mångfald. Utifrån begreppen assimilation, sammanbindning och mångkulturell inkorporering studeras skolans mottagande av nyanlända elever. Med exempel från Malmö, Toronto och London (Ontario), diskuteras vad som sker i det första mötet med skolan och var de nyanlända eleverna placeras. Vidare diskuteras hur modersmålslärarnas arbetsuppgifter i den svenska skolan skiljer sig från de arbetsuppgifter som de kanadensiska ”settlement workers” har och vilken roll som civilsamhället eventuellt kan ha för att bidra till en ökad integration. English abstract This article takes as its point of departure a comparison between Swedish and Canadian educational policy and between how the two countries deal with issues of integration and diversity. Focus of the study is the reception of new arrivals in schools, based on the concepts of assimilation, hyphenation and multicultural incorporation. Using examples from Malmö, Sweden and Toronto and London (Ontario), the article asks questions such as: where are the new arrivals being placed and what happens in the initial meeting with the schools? Further lines of inquiry concern the differences between work tasks of the Swedish ”modersmålslärarna” and the Canadian settlement workers as well as what role civil society plays in the work towards increased integration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".