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Urban impoverishment and multiple modes of livelihood in Ghana

2001· article· en· W2011888982 on OpenAlexvenueno aff
Francis Owusu

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
FundersRockefeller Foundation
KeywordsLivelihoodRestructuringPovertyEconomic growthBusinessCapital (architecture)Development economicsEconomicsGeographyFinanceAgriculture

Abstract

fetched live from OpenAlex

Many African countries experienced economic crisis in the 1970s and are currently restructuring their economies under the tutelage of the World Bank and the IMF. The restructuring process has had pervasive effects on the livelihood strategies of many people, as their established means of income generation have been disrupted. While survival of the urban poor has been studied, little is known of strategies of other social groups. Using Ghana as a case study, I argue that although urban poverty predates the implementation of structural adjustment program (SAP), the policies have created a favorable environment for the intensification of multiple livelihood strategies among of salaried employees. The paper finds that multiple livelihood strategies are practiced by a large number of salaried employees, but their involvement depends on many factors, including individual, family and household characteristics; access to capital and resources; opportunities offered by the urban economy; and the nature of formal employment.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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

Citations30
Published2001
Admission routes1
Has abstractyes

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