Interrogating the “Complementarity” of Complementary Pathways: A Transnational Evaluation of Policy, Practice and Sustainability
Bibliographic record
Abstract
Abstract The rise of “complementary pathways” poses challenges to international refugee protection. This Special Issue consists of seven original and interdisciplinary articles based on empirical research, which examine the legal, normative, and practical implications of attempts to broaden protection to incorporate “complementary pathways” in responding to the global challenge of increasing numbers of people in need of international protection. The articles provide legal, normative, and policy perspectives on domestic, regional, and international law and practice. They cover programmes in Australia, Canada, Ireland, New Zealand, Spain, and the United Kingdom, as well as European policies. They present refugee sponsorship programmes as a potentially “sustainable” complementary pathway, with opportunities to create “sustainable communities” of sponsors to complement state refugee resettlement schemes. However, the picture is not the same for other complementary pathways, which risk reinforcing a tiered system of refugee rights and excluding those most in need. The contributors to this Special Issue highlight that more safeguards are needed to ensure that complementary pathways do not undermine or deprioritise the international protection needs of refugees. Collectively, this Special Issue offers fresh and compelling insights into the risks, potential, and realities associated with the expansion of complementary pathways.
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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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".