Bibliographic record
Abstract
While democracy has many benefits and is undoubtedly the favorite political system in the international community, it has some draw backs. In Africa, a major effect of democratization is the decline in open door asylum. Unsuccessful transitions in some countries have contributed to an increase in conflict resulting in higher numbers of displaced people on the continent. However, during this era most African states particularly those that are noted to have experienced some success in their democratic transitions, have implemented restrictive asylum practices. These include denial of asylum, detention, deportation of asylum-seekers, increase in cases of refoulement and less protection, social and economic support for those granted refugee status. These practices are inconsistent with human rights principles which are fundamental to democracies. Additionally, the adoption of such restrictive practices contradicts the post-independent era, where African states prior to the 1990s are noted to have implemented open door asylum. The objective of this paper is to analyze the link between democratization and the end to open door asylum in Africa. Using the cases of South Africa and Ghana the chapter suggests that some elements of democratic practices such as elections and the demand on states to institutionalize asylum practices through the enactment of national asylum laws, have contributed to the displacement of open door asylum on the continent.
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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 | 0.001 | 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 | 0.008 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".