Effectiveness of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) in Curbing Elephant Poaching in Zimbabwe
Bibliographic record
Abstract
The research focused on understanding the effectiveness and applicability of CITES in curbing elephant poaching in Zimbabwe. CITES regulates international legal trade in ivory in an effort to curb poaching and this is addressed by the theory of complex interdependence. Signatory states adhere to the provisions of CITES but with all this in place elephant poaching is on the rise across Africa and Zimbabwe in particular. Zimbabwe relies on wildlife for tourism thus the threat to extinction is a threat to national revenue. Key informants were purposively sampled and documentary research was used for the case study. The main findings were that poaching Zimbabwe has become very rampant in the past few years with highest numbers recorded between 2012 and 2015. This has been attributed to the economics of demand and supply where high demand for ivory in Asian markets with countries such as China becoming the world’s largest destination market for illegal ivory. On the supply side, Zimbabwe is facing economic challenges thus locals are now engaging and aiding in poaching for economic survival. The research concluded that CITES weaknesses is in that it only provides state parties with technical support thus without the financial support anti-poaching efforts are ineffective.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".