Being a helping professional in a transnational context: A framework of practice with forced returnees
Bibliographic record
Abstract
In 2015, the European Union experienced a 51% increase in asylum requests. Kosovars constituted the fourth largest group of these asylum seekers, yet only 4% were granted asylum. Rejected applicants continue to be forcefully returned to Kosova partly because repatriation, or the right to return to one’s country of origin, is the EU’s preferred solution to migration crisis. This despite a significant body of research which substantiates that repatriation is largely involuntary and not a durable solution. To address the discrepancy between existing evidence and the adoption of repatriation as a sustainable solution, my study employed Critical Discourses Analysis to explore the involuntary repatriation of rejected asylum seekers from Kosova. Findings from semi-structured interviews with rejected asylum seekers suggest that this population employ discourses which construct EU countries as superior to Kosova and migration to these countries as an opportunity for a better life. These discourses contribute to how returnees approach repatriation, as well as inform the practice of helping professionals. Responding to calls for models of practice which guide the work of helping professionals with forced returnees, this paper builds upon study findings centering dominant discourses to develop a framework across micro, mezzo and macro levels of practice.
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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 | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.036 | 0.090 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".