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Record W2224379877

The Business Response to HIV/AIDS: What it Tells Us About Botswana's 'Variety of Capitalism'

2013· article· en· W2224379877 on OpenAlexaff
Antoinette Handley

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrivate sectorCapitalismVariety (cybernetics)Government (linguistics)ElitePoliticsBusiness sectorPolitical economyPublic sectorPolitical scienceConstructiveEconomic growthPublic policyDevelopment economicsEconomyEconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

While the government of Botswana is frequently lauded for its astute management of the economy and well-funded policy response to HIV/AIDS, less attention is often paid to the response by the country’s private sector – that was, if anything, even earlier and more remarkable than that of the country’s public sector. This paper examines the political economy of the business response to HIV/AIDS in Botswana, seeking to understand why important sectors of business responded as they did. The paper uses the epidemic and the dramatic challenges that it posed for the economy as a case study of how private sector elites came to understand and price risk in the midst of tremendous uncertainty and an unfolding social crisis. How businesses in Botswana come to understand their own interests – and why they come to see a constructive response to the epidemic as key to their interests- reveals a great deal about the variety of capitalism that is evolving in Botswana. The paper draws on fieldwork in Botswana, extensive elite interviews and insights gleaned from a comparative study of the same issues in South Africa, Kenya and Uganda.

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)0.004
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.008
Scholarly communication0.0060.004
Open science0.0000.002
Research integrity0.0020.003
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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