Improved decision-making processes for the transfrontier conservation areas of southern Africa
Bibliographic record
Abstract
The focus of this research is environmental governance in Africa, explored through the lens of trans-border conservation initiatives. I used the embedded case study approach to dissect the political, socio-economic and ecosystem management aspects of decision making in the establishment and management of protected areas across national boundaries, focusing on two transfrontier conservation areas (TFCAs) in southern Africa, the Greater Limpopo and the Greater Mapungubwe transfrontier conservation areas. This is a qualitative study using mixed methods to collect data, including 93 semi-structured interviews with current and potential decision makers from every possible level, 16 questionnaires, ten mental model workshops, several meetings with local municipalities and other decision-making platforms, and an in-depth scrutiny of relevant policies and treaty documents. Interviewees provided inputs into a value system framework based on a compilation of attributes from each of the ecosystem, socio-economic and governance literature, to produce an average score for each of the two case study areas. The results indicated highly disjunctive approaches among countries forming part of the TFCAs, leading to many undesirable feedback loops. The decision-making processes of each country component of the two TFCAs were then analyzed separately, using a “governance” capability maturity model to determine the effectiveness of current management practices. A “collaboration” maturity model was used to identify gaps in the information sharing, decision making and patterns of interaction among the different stakeholders of each of the two TFCAs, indicating institutional and decision-making flaws in the current system. Some recommendations are provided to improve these in order to overcome current failures in the three dimensions of a TFCA.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".