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Record W1978466102 · doi:10.1080/23322373.2015.994423

Place Images and Nation Branding in the African Context: Challenges, Opportunities, and Questions for Policy and Research

2015· article· en· W1978466102 on OpenAlexaff
Nicolas Papadopoulos, Leila Hamzaoui-Essoussi

Bibliographic record

VenueAfrica Journal of Management · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of OttawaCarleton University
Fundersnot available
KeywordsTourismContext (archaeology)Nation brandingAffect (linguistics)Political scienceChinaPerspective (graphical)MarketingEthnocentrismPublic relationsSociologyBusinessGeography

Abstract

fetched live from OpenAlex

This paper examines nation branding in Africa, based on a comprehensive and integrative review of past research. This considers, among others, current place images on the continent in the context of traditional views, which see it as a poor and underdeveloped region, versus the newer perspective of an evolving “African Renaissance”; efforts at systematic place marketing in sectors including tourism, foreign investment, natural resource and manufactured exports, and higher education; the absence of intra-African research on place image effects; reverse ethnocentrism, or the generally positive view of African consumers about products originating from more developed countries outside the continent; and the “continent effect”, which may affect individual countries' efforts to establish their own identities. Information and insights from this review are synthesized to highlight implications and potential directions for future research and policy, on both specific thematic areas as well as overarching research topics that affect all aspects of African images and nation branding.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0030.009
Scholarly communication0.0080.011
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.346
GPT teacher head0.430
Teacher spread0.084 · 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 designTheoretical or conceptual
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

Citations38
Published2015
Admission routes1
Has abstractyes

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