Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".