SVERIGE OCH KANADA I FÖRÄNDRING? En kvalitativ analys av politisk diskurs fokuserad på Mångkulturalism och Civic integration
Bibliographic record
Abstract
"Sweden and Canada in transformation? - A qualitative analysis about governmental rhetoric focused on multiculturalism and civic integration.) Integration stole the spotlight and became the focus of political discussions after some intense years of increased immigration. Rising fears about failed socio-economic and socio-political integration caused many states to make changes in their integration policy's. However, the content varies considerably between countries and do not always reflect the state of the political discourse. Therefore, this study analyses political statements using ideal types to find out whether two specific integration concepts, multiculturalism and civic integration, that has gotten much attention in both academic and political circles, can be found to have changed in Sweden's and Canada's political discourse. It seeks to describe specifically if the political discourse on the highest level focused on civic integration and multiculturalism, has changed due to increased immigration and in face of new challenges between 2011-2017. The result implies that Canada's political discourse 2011 reflected multiculturalism and then fluctuated between the years investigated but turned back to being more coloured by multiculturalism. The same fluctuating pattern is found in results for Sweden's political discourse, but the country's discourse stronger reflected civic integration both 2011 and 2017.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".