Revisiting the Politics of Asylum in Africa: Explaining Kenya's Sub-National Policy Variation
Bibliographic record
Abstract
The politics of asylum in Africa are changing.In recent years countries in Africa have taken increasingly different approaches from one another in the development, interpretation, and implementation of asylum policy.However, the advent of such variation is not just occurring between sovereign states, but also within them.It is no longer possible to refer to a generalized 'politics of asylum in Africa,' nor is it possible to omit discussions of sub-national politics from our analyses -as I contend that they play a valuable role in determining policy outcomes.To understand these changes, I ask what explains contemporary sub-national variation in the politics of asylum in Africa's major refugee hosting states?This dissertation uses Kenya to conduct a within-case comparison that examines the interpretation and implementation of Kenya's national asylum policy in its three major refugee hosting regions: the Dadaab refugee camps in Garissa County, the Kakuma refugee camps and the Kalobeyei Integrated Settlement in Turkana County, and the urban refugee population in Kenya's Capital City, Nairobi.This dissertation argues that sub-national variation in Kenya has been facilitated, at least in part, by the introduction of the 2010 Constitution and the introduction of devolved governance to the country.Replacing Kenya's previously highly centralized political structure with democratically accountable county governments is expected to influence the political calculus of local elites, creating space for more diverse policy options in these contexts.As the scope of what is politically possible widens, so too does the potential for mutually beneficial outcomes that support both the refugee and local host populations.The politics of devolution in Kenya, while a key facilitating factor, is not the sole influence on the outcomes observed in each major refugee hosting region.I further argue that additional contextual factors unique to each major refugee hosting area such as the level of involvement from the central government, refugee population demographics, and local political opportunity structures work to shape and condition the limits of political possibilities at the sub-national level.iii Dedication This dissertation is dedicated to the memories of my late grandmothers: Joan Horner & Sandra Barkley.Neither were able to see me achieve this milestone, but their love and support carried me through to the end.Table 1 -Variables Affecting Asylum Policy in Kenya's Major Refugee Hosing Areas…………………….…….………………………………………………… 13 ix List of Tables
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".