Human engagement and great ape conservation in Africa
Bibliographic record
Abstract
Despite large investments of funding into great ape conservation in Africa, wild populations of gorillas (Gorilla ssp), chimpanzees (Pan troglodytes ssp) and bonobos (Pan paniscus) continue to decline. Causes for this decline fall into three broad categories: habitat loss, illegal hunting, and disease. Contributing factors to all of these causes are linked to pressure from the expanding human population competing for forest resources. We have moved beyond the time of debating the pros and cons of including human engagement activities in conservation. If humans are part of the problem, they must also be part of the solution. To move our understanding of which human engagement activities are effective, what methodologies are being used and best practices for setting up a successful framework, we interviewed practitioners representing 53 projects working in great ape habitat in Africa. The interviewees represented almost 900 years of experience with African great ape conservation. We found that all practitioners agreed that for conservation to succeed, projects must engage with humans utilizing resources from great ape habitats. However, evaluation of such work was elusive. Projects that employed at least one person designated as an educator were more likely to have structured programs, regular engagement activities, and to evaluate their work. To date, little information on the success or failure of the activities has been published, thus perpetuating the problem of relying on personal experience rather than evidence when developing new engagement programs. Additionally, linking human engagement activities to biological impact remains a challenge. The results presented in this paper demonstrate the importance placed on human engagement activities to effectively conserve great apes in Africa while at the same time identifies gaps in our understanding on the link between such activities and project success.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".