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Record W2903107994 · doi:10.1177/0276146718815937

A Historical Review of Local Intermediaries in Impoverished Contexts

2018· review· en· W2903107994 on OpenAlexaff
Stefanie Beninger, Stanley J. Shapiro

Bibliographic record

VenueJournal of Macromarketing · 2018
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsSimon Fraser University
FundersWorld Bank Group
KeywordsIntermediaryScholarshipWork (physics)SociologyPublic relationsPolitical scienceBusinessMarketingEconomicsPositive economicsEconomic growth

Abstract

fetched live from OpenAlex

The role of local intermediaries in impoverished contexts has been a focus of attention within the marketing and development literature since shortly after WWII. A systematic review of this work reveals that while much of this literature identifies market intermediaries as exploitative and unspecialized entities, other work views them more positively. These conflicting accounts, rarely supported empirically, have resulted in diverging policy recommendations. Many scholars call for the wholesale removal of such intermediaries, while a lesser number argue that they can and should be supported. The dominant view as regards local intermediaries reflects three prevailing biases in the marketing and development literature: an emphasis on transformation; a rejection of the local; and a favouring of consumers and producers over intermediaries. This paper describes these biases and proposes shaping constructs, potential future research questions, theoretical lenses, and methods of analysis that should ideally guide future scholarship on the role of local intermediaries within impoverished contexts.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.016
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.286
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2018
Admission routes1
Has abstractyes

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