Adapting Community Sponsorship : The Role of Civil Society and Policy Mobility in Swedish Local Governance
Bibliographic record
Abstract
A few select Swedish municipalities have recently introduced pilot projects of the Canadian Community Sponsorship integration policy, which is based on collaboration between civil society and the public sector in refugee reception. Departing from a framework of collaborative governance and policy mobility, this study aims to examine the collaboration between the public sector and civil society and the relationship between the community sponsorship project and policy mobility. Six semi-structured interviews with representatives of municipalities, civil society organisations and UNHCR served as a basis in answering how the project can be understood via policy mobility and how the cooperation takes shape, thereby contributing to the fields of cross-sector co-production and policy mobility. Results show that pre-existing relationships between civil society and municipalities have strengthened the collaboration. However, this collaboration blurs lines between new policies and existing practices - thus raising questions on the necessity of adopting this mobile policy. The study also suggests the legitimacy added by UNHCR as an explanation, whilst highlighting the mobility possibilities attributed to Länsstyrelsen. Recommendations include: providing physical meeting spaces, highlighting the uniqueness of the policy and looking into power dynamics to ameliorate the policy adaptation and enhance the mobility potential.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| 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".