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Record W2288762960 · doi:10.1177/0020702015619566

International development and the private sector: The ambiguities of “partnership”

2015· article· en· W2288762960 on OpenAlexaff
David Black, Ben O’Bright

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPrivate sectorAmbivalenceGeneral partnershipForeign direct investmentEmpowermentEconomicsCorporate social responsibilityPrivate sector involvementMarket economyPolitical economyEconomic systemEconomic growthPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

Historically, the relationship between the private sector and international development has been deeply ambivalent. For many, a vibrant private sector and competitive markets are the essential prerequisites of development. For many others, development is principally concerned with ameliorating the dislocation associated with capitalist profit seeking. In the last generation, this ambivalence has given way to an emphasis on the complementarities between the private sector and development. Yet skeptics have continued to criticize the form of development this trend has promoted. We review the historical conditions behind this trend; the controversies concerning transnational corporations and foreign direct investment; the rise of corporate social responsibility; the parallel rise of philanthrocapitalism; and the growth of micro-credit as a market-oriented vehicle for poverty alleviation and empowerment. When taken together, it is clear that private sector actors have become increasingly influential in the new landscape of development, yet their effects remain ambiguous.

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)0.013
metaresearch head score (Gemma)0.016
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0070.050
Scholarly communication0.0170.018
Open science0.0010.009
Research integrity0.0060.008
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 head0.030
GPT teacher head0.328
Teacher spread0.298 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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