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The Perils of Unmanaged Export Growth: The Case of Kava in Fiji

2006· article· en· W2113886934 on OpenAlexaff
Naren Prasad, Shiu Raj

Bibliographic record

VenueJournal of Small Business & Entrepreneurship · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLivelihoodKavaContext (archaeology)Per capitaProduct (mathematics)Niche marketGross domestic productDeveloping countryDevelopment economicsBusinessScale (ratio)International tradeEconomicsEconomic growthAgricultureGeographyMarketing

Abstract

fetched live from OpenAlex

Abstract Small developing countries are increasingly marginalized in the global system, especially in the context of global trading. There is a fascination for “bigness” and the global system exerts pressure, at times challenging the very existence of smaller countries. However, there are several small island economies that have succeeded in formulating ingenious development policies to overcome their vulnerabilities. Numerous smaller countries have relatively higher GDP per capita or HDI compared to the rest of the world. One way of overcoming problems associated with smallness is to find niche products for a sizeable international market. This article looks into how small-scale informal sector activities in a small island country could prove to be a pacesetter for a niche product in international trade. It explores the extent to which Fiji’s small-scale farmers have contributed to the exports of kava, and discusses its impact on their livelihoods and the challenges for the kava industry. This article also highlights how success could breed failure when growth is unmanaged, quality is not guaranteed, and domestic product regulation inexistent.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations13
Published2006
Admission routes1
Has abstractyes

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