What is the evidence that counter‐wildlife crime interventions are effective for conserving African, Asian and Latin American wildlife directly threatened by exploitation? A systematic map protocol
Bibliographic record
Abstract
Abstract Human activities are driving a global biodiversity crisis. In response, a broad range of conservation actions have been implemented. With finite resources available, and a rapidly narrowing window, the scientific and policy communities have acknowledged the need to better understand the effectiveness of interventions for conserving threatened species. Given the recent emphasis on the use of counter wildlife crime interventions (i.e. those that directly protect wildlife from illegal harvest, detect and sanction rule‐breakers, and interdict and control illegal wildlife commodities), there is a clear need to summarize the available evidence on biological and threat reduction outcomes of such actions to help make evidence‐informed management and funding decisions. Here, we present a protocol for a systematic map that will collate the existing body of literature addressing the effectiveness of counter‐wildlife crime interventions for protecting targeted species. Our focus will be on select species or species groups directly threatened by exploitation (i.e. illegal harming whether by harvest as a resource or for control/persecution) and native to Africa, Asia and Latin America, which are regions that have experienced significant wildlife populations declines. The systematic map will aim to capture available evidence found in commercially published and grey literature. We will search for the literature using four publication databases, Google Scholar, 36 specialist websites and databases and sources identified through a call for evidence among relevant networks. Eligibility screening will be conducted at two stages: (1) title and abstract and (2) full text. Relevant information from included papers will be extracted and entered into a searchable, coded database (MS‐Excel). Narrative synthesis and descriptive statistics will describe the key characteristics of the relevant evidence base (e.g. geographic location, species, interventions, direct threats, outcomes and study designs). Using visual heat maps, we will identify key knowledge gaps warranting further research and clusters of evidence that could serve as topics for future systematic reviews. The resulting map will guide further exploration on evaluating the effectiveness of counter‐wildlife crime interventions, and aid in building an evidence base that supports both management and funding decisions to ensure efficient use of limited resources and maximal conservation benefits.
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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 | 0.080 | 0.167 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.032 | 0.018 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.096 | 0.016 |
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".