Bibliographic record
Abstract
The recent and rapid increase in wildlife crime not only threatens the survival of significant populations of endangered species in South Africa, but also threatens regional security, the sustainability of the tourism sector and social stability of local communities. Many interventions and actions in addressing wildlife crime fail to achieve sustained impact mainly due to the complexity of the problem and the resulting multiple and simultaneous interventions needed along the short, medium and long term. Factors that contribute to the complexity of the problem are, the number of role players involved, the framing of the problem through different worldviews, the high stakes, the number of simultaneous aspects of interventions, the problem dynamics and the huge number of interactions. Different aspects of the problem are interconnected, but stakeholders are tempted to address the problem in parts (fragmentation), thus creating new problems. This dynamic facilitates situations in which decision makers find the problem too big and complex to address and they remain in a state of crisis management. Addressing the current wildlife crisis requires harmonised efforts incorporating on-the-ground cross-border cooperation and a strategic environment that balances conserving wildlife with stakeholder needs for economic growth and local, national, and regional stability. This paper explores innovative and integrated ways in mitigating the complexity of the wildlife crime problem. The approach is problem focused as opposed to discipline focused or organisation-centric. The paper also discusses the lessons learnt and the resulting preliminary set of “principles”: inclusivity of actors, different ways of being and knowing as ways of addressing fragmentation; foresight; governance, as dynamic problem solving to build capabilities; and the transforming organisational narratives as part of implementing new strategies. These “principles” form the basis of the whole-of-society approach in dealing with complexity and can be applied in future interventions that concentrate on combining operational and scientific expertise with local knowledge, through participatory learning and governance.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".