How Do Consociations Craft Asylum Policy? Lebanon’s Response to Conflict-Induced Displacement as an Exploratory Case
Bibliographic record
Abstract
Abstract How do consociations craft their asylum policy, and how do they deal with the rights of “others”? Research has started to explore the relationship between consociational governance and non-ethnic or non-sectarian social groups. Yet, we still know little about how consociations interact with refugee flight on the one hand, and with the ethics of refugee protection on the other. As a form of thick institutional complexity, consociationalism risks limiting the ability of the state to respond to refugee displacement in a manner that is timely, effective, and which respects the rights and dignity of displaced individuals. We draw on Lebanon’s response to the arrival of some 1.5 million displaced individuals in the country since the start of Syria’s lethal conflict in 2011 as an exploratory case study that seeks to further knowledge on how consociations craft and implement their asylum policy. Specifically, we consider three mechanisms of immobilism that constrain the crafting of unified, responsive, and inclusive asylum policy and that are posed by the consociational state apparatus itself: ethnicization/sectarianization, procrastination, and fragmentation. Building on Lebanon’s patterns of refugee policymaking, we show how these mechanisms mutually reinforce one another, backfiring on policy congruence as well as refugee rights and 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".