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Record W2994203424 · doi:10.4102/ink.v9i1.207

Contextual background to the rapid increase in migration from Zimbabwe since 1990

2017· article· en· W2994203424 on OpenAlexaboutno aff
Crescentia Madebwe, Victor Madebwe

Bibliographic record

VenueInkanyiso · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationUnemploymentQuarter (Canadian coin)PovertyPopulationDevelopment economicsGeographyDemocracyPoliticsPolitical instabilityPolitical scienceEconomic growthDemographic economicsEconomicsDemographySociology

Abstract

fetched live from OpenAlex

This paper provides a contextual background to and causes of recent emigration from Zimbabwe. With an estimated quarter of the population currently living outside Zimbabwe, migration from the country is unprecedented. The country is now ranked as one of the top ten migrant-sending countries in subSaharan Africa that include Mali, Burkina Faso, Ghana, Eritrea, Nigeria, Mozambique, South Africa, Sudan and the Democratic Republic of Congo. Periods of migration are divided into sections, beginning with the war of liberation (1960-1979) to 1990; 1991 to 1997 and 1998 and beyond. Migration was caused by inter-related factors ranging from political and economic instability, poverty, low returns to labour, unemployment, increased informalisation of the economy, fluctuation in prices of basic commodities and their erratic supply. Migrants from Zimbabwe are a diverse combination of people of all ages that include professionals, semi-skilled and unskilled workers, documented and undocumented migrants dispersed in countries in the region, predominantly South Africa and Botswana, and far-flung countries like the United Kingdom, the United States of America, Canada, Australia and New Zealand. Whereas in the past male migration was dominant, by 2000 women have migrated in almost equal numbers with men.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.320
Teacher spread0.287 · 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.

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

Citations16
Published2017
Admission routes1
Has abstractyes

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