The myth of free-riding: Refugee protection and implicit burden-sharing
Bibliographic record
Abstract
Why do states accept what appear to be disproportionate and inequitable burdens in the provision of international collective goods? Traditional burden-sharing models emphasise free-riding opportunities of small countries at the expense of larger ones. An alternative model suggests that countries specialise according to their comparative advantage as to the type and level of contribution they make to international collective goods. We apply this model to forced migration and suggest that countries can contribute to refugee protection in two principal ways: proactively, through peacekeeping/making and reactively, by providing protection for displaced persons. While the existing literature on peacekeeping provides evidence for the ‘exploitation of the big by the small’, our analysis of UNHCR data of 15 OECD countries for the period 1994–2002 balances this view by showing that reactive burdens are disproportionately borne by smaller states. We also show that EU asylum policy initiatives directed at refugee burden-sharing aim at equalising particular dimensions of states' contributions to refugee protection. By doing so, they curtail opportunities for specialisation and risk consolidating a sub-optimal provision of refugee protection.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 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".