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Record W4238801568 · doi:10.14426/ahmr.v3i2.824

Benign Neglect or Active Destruction? A Critical Analysis of Refugee and Informal Sector Policy and Practice in South Africa

2017· article· en· W4238801568 on OpenAlexaff
Jonathan Crush, Caroline Skinner, Manal Stulgaitis

Bibliographic record

VenueAFRICAN HUMAN MOBILITY REVIEW · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsBalsillie School of International Affairs
Fundersnot available
KeywordsRefugeeBureaucracyPolitical scienceLivelihoodNeglectCorporate governanceEconomic growthConfusionPovertyDevelopment economicsLawEconomicsGeographyManagement

Abstract

fetched live from OpenAlex

To fully comprehend the disabling policy environment in which refugees in South Africa attempt to carve out a livelihood, it is important to analyse two largely independent but overlapping streams of policy-making. This paper first examines the post-apartheid refugee protection regime and traces how and why a generous right-based approach has been progressively comprised by growing restrictionism, exclusion and bureaucratic ineptitude. The 2017 Refugees Amendment Act and White Paper on International Migration represent the culmination of this process. While both are probably unimplementable and will be the subject of numerous court challenges, they can be seen as a major retreat and an increasing failure to protect. The second part of the paper traces the history of national and municipal informal sector governance since the early 1990s. Since so many refugees are forced or choose to work informally, the uncertainty and confusion this history has produced is of particular relevance. Refugee entrepreneurs have regularly been the victims of general and targeted informal sector eradication campaigns.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.423
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2017
Admission routes1
Has abstractyes

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