Strength through diversity’s Spotlight Report for Sweden
Bibliographic record
Abstract
Within OECD countries, Sweden has historically welcomed large numbers of migrants, in particular migrants seeking humanitarian protection. Since 2015, this large influx of new arrivals with multiple disadvantages has put a well-developed integration system under great pressure and highlighted a number of challenges for education policy given current institutional frameworks. PISA 2015 shows that immigrant students fare considerably worse than native students in terms of academic and well-being outcomes also after accounting for differences in social-economic background. The OECD has identified four priority areas for Sweden for closing the gap between immigrant and native students: (1) Facilitating the access of immigrants to school choice, (2) Building teaching capacity, (3) Providing language training and (4) Strengthening the management of diversity. The findings in this Spotlight Report are based on existing OECD work in the area of immigrant integration in education, OECD and national data, a questionnaire on the range of policies and practices in Sweden and good practice examples for the integration in the education system in peer-learner countries and regions [Austria, Germany, the Netherlands and North America (Canada and the United States)], which were identified of particular relevance for Sweden. The report also includes policy pointers on what policies and practices Sweden could adopt to respond to the current integration challenges in the four priority areas.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.040 |
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".