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Record W2419487583 · doi:10.5539/jsd.v9n3p192

Uganda’s Revealed Comparative Advantage in COMESA

2016· article· en· W2419487583 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsRevealed comparative advantageBusinessDiversification (marketing strategy)Comparative advantageInternational tradeAccessionAgricultureProductivityAgricultural economicsEconomicsEconomic growthEuropean unionMarketing

Abstract

fetched live from OpenAlex

Most recently, Uganda increased its trade engagements with COMESA as demonstrated by its submission of accession instruments to COMESA Secretariat in order to access the Free Trade Area (FTA). It is envisaged that trade with COMESA can compensate for the low export demand elsewhere by enabling diversification of the export basket and facilitating value addition to traditional exports. It is also expected to enhance producer competitiveness and consumer welfare. Full exploitation of this requires information on where and in what commodities Uganda’s trade niche lies. This study assesses the country competiveness within COMESA based on the concept of Revealed Comparative advantage (RCA). The paper also evaluates the stability of Uganda’s RCA in COMESA from 1997-2014 using HS6-digit level export and re-exports data obtained from the World Integrated Trade System. Findings reveal that Uganda’s RCA is in all 16 industries at the product chapter level. It is stable in exports of animals, vegetables, food production, wood, textiles, & cloth, stone & glass and metals. Policies for further development of these sectors should aim at addressing sectoral challenges including the low productivity, marketing, and processing capacity in the animal sector, low capacity to test phytosanitary and sanitary certification in the vegetable sector. Additionally, tackling market and low production challenges for the textile sector and, high costs of production for the metals sector will further boost exports to the region.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.241
Teacher spread0.222 · 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