A social pedagogy lens for social work practice with return migrants
Bibliographic record
Abstract
The number of asylum rejections has increased in recent years, yet successful claims differ dependent on the originating county of the asylum seekers. In 2018, the European Union rejected 25 per cent of the 519,000 asylum requests which it received (Eurostat, 2019). Kosovars were the fourth-largest group of asylum seekers in Europe in 2015 and 96 per cent of them were rejected and returned to Kosova. Rejected asylum seekers and those who lose their temporary status are returned to their countries of origin partly because the EU endorses repatriation, or the return of forced migrants to their country of origin, as a preferred solution to the migration crisis. This, despite a significant body of research which substantiates that repatriation is not sustainable and current repatriation policies have seldom considered the experiences of rejected asylum seekers. Considering that social workers are the first point of contact for many rejected asylum seekers, models of practice which inform social work with this population are needed. This article uses the case of Kosovar returnees to examine the utility of a social pedagogy lens to better prepare social workers to work with returnees. Social pedagogy, with its dedication to social justice, the importance it places on local and regional contexts, as well as its attention to praxis, is well placed to guide social workers in partnering with return migrants as they navigate the complex realities of reintegration. While grounded in Kosova’s context, the social pedagogy framework has global implications considering the increasing number of return migrants worldwide.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.061 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| 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".