The Implications of Non-tariff Barriers to Trade on COMESA Free Trade Area: The Case of Zimbabwe and Zambia
Bibliographic record
Abstract
This research sought to explore the implications of Non-Tariff Barriers (NTBs) to trade on the Common Market for Eastern and Southern Africa (COMESA) Free Trade Area (FTA). If the COMESA free trade area is appropriately dealt with, COMESA members and trade stakeholders will get information that is important in their attempt to attain the goal of eliminating trade impediments within the region. This will promote regional economic integration and enhance growth through increased investment levels; scaled up exchange of goods and services; and enhanced socio-economic cooperation. Such cooperation will directly contribute to the improved political and trade relations. The research adopted a case study design in which various cases were examined to understand issues surrounding the implications of NTBs on COMESA free trade area. A qualitative research methodology was also utilised while data was collected through key informant interviews and document analysis. The research concluded that NTBs in COMESA FTA are used on health issues as well as to protect the infant industries in the region. The research therefore recommended that COMESA members find a working definition of what constitute an infant industry for the purpose of applying for derogation; and also that they make use of bilateral trade agreements to eliminate existing NTBs where States clearly indicated their objectives of removing all NTBs that inhibit trade between them.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".