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Record W2901233907 · doi:10.1080/02589001.2018.1547373

‘Things are not working now’: poverty, food insecurity and perceptions of corruption in urban Malawi

2018· article· en· W2901233907 on OpenAlexaff
Liam Riley, Emmanuel Chilanga

Bibliographic record

VenueJournal of Contemporary African Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsMcGill UniversityWilfrid Laurier UniversityBalsillie School of International Affairs
Fundersnot available
KeywordsPovertyFood insecurityUrban povertyLanguage changeWorking poorDevelopment economicsPerceptionFood securityDeveloping countryEconomic growthPolitical scienceSocioeconomicsGeographyEconomicsPsychologyAgriculture

Abstract

fetched live from OpenAlex

Malawi is one of the world's least urbanised countries, but its cities are growing rapidly and poverty in urban Malawi is becoming a prominent political issue. Food insecurity is a widespread manifestation of urban poverty in Africa, especially in informal settlements. This article is based on in-depth interviews with food insecure residents of Lilongwe’s informal settlements who, when asked why they were food insecure, overwhelmingly pointed to the Cashgate corruption scandal as a cause. There have been many political corruption scandals in Malawi, but the Cashgate scandal, which was revealed in September 2013 and reverberated throughout the political culture, has been among the most prominent and consequential of these scandals. The article seeks to contribute to literature on the political dimensions of urban food security in Africa while also presenting a way of understanding corruption from the point of view of vulnerable people whose lives have been directly and indirectly affected.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.321
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations49
Published2018
Admission routes1
Has abstractyes

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