2.11-P9Adaptation of private sponsorship approach for Syrian refugees living in Turkey- Learning from collaboration
Bibliographic record
Abstract
Issue: The number of Syrian refugees living in Turkey has increased to three million within a few years. Humanitarian and social support from local to refugee families is a civil society tradition in Turkey. However, this practice is never structured, or managed by an organisation. Government-assisted private sponsorship of refugees involves standardised support managed by a public body and is the main approach adopted by the Canadian government. Description of the practice: A standardised model of civil society support to refugee families is likely to lead to identification of the refugee families that will most benefit from the support, prevention of misuse or abuses from both sides and equity in support. It should also change aid giving culture in a positive direction. However, developing a model requires academic input to the development of services. Results: AKDEM, a municipality health and social services centre in Istanbul, has been serving refugees with constant improvements in the last few years. Academic support to their work is aimed at systematising the processes, ensuring accountability, contributing to planning with a focus on evaluation and assessing the suitability of the model for Turkey. Representatives from AKDEM, an NGO and a university based in Istanbul formed a team and carried out consultations with possible supporting families, the Syrian community, the Ministry of Families and Social Support, and local health managers to develop the principles and criteria of the programme. The criteria for selection of Syrian refugee families, the responsibilities of both sides and evaluation questions and design were determined through lengthy discussions. Conclusions: Although yet to be implemented, the refugee support model was adapted to the Turkish environment, after a year long but informative experience for all the parties involved. Main message: Local government benefits from university involvement in the development of a refugee support programme even before it is implemented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.009 |
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".