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Record W1044011948 · doi:10.4324/9780203710807-7

Market development in the African context

2020· book· en· W1044011948 on OpenAlexaff
Benét DeBerry‐Spence, Sammy K. Bonsu, Eric J. Arnould

Bibliographic record

VenueMarketing management · 2020
Typebook
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsYork University
Fundersnot available
KeywordsContext (archaeology)EconomicsGeography

Abstract

fetched live from OpenAlex

Africa has moved from the periphery of interest to global business to the center as Euro-American concerns re-evaluate its potential and emerging market actors in China and India continue to expand their presence. This chapter proposes an approach to African market development premised on the view that globalization–that which affords enhanced free mobility of resources around the world–portends an increasing role for Africa and its markets in the future world economy. However, the realities that African market actors and institutions face are similar to those faced by their Western counterparts, including supply, demand, and the allocation of resources to address market needs, even if the mechanisms employed in the African context are less formal. Although the lack of infrastructure, low levels of education, high levels of corruption, and other negative factors are indeed present in the African context, poor market development by firms appears to be grounded more in neo-colonial misconceptions than the realities of business operations in Africa.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.004
Scholarly communication0.0050.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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
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

Citations3
Published2020
Admission routes1
Has abstractyes

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