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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it