Measuring the impact of an entertainment‐education intervention to reduce demand for bushmeat
Bibliographic record
Abstract
Abstract The trade and consumption of bushmeat are a major threat to biodiversity across the tropics. Conservationists have traditionally advocated for stricter regulation and enforcement as a way to control these practices, with less attention given to consumers and the management of the demand. Yet, it is clear that without adequately tackling demand, it is impossible to effectively curb the bushmeat trade. In this paper, we describe an intervention to reduce demand for bushmeat in northern Tanzania. The intervention was centered around the 1‐h radio show My Wildlife – My Community which included 15‐min episodes of the radio drama Temboni . Each episode of the radio drama was accompanied by a 45‐min interactive call‐in show featuring interviews with experts and local information about available community resources. We evaluated this intervention using a Before‐After‐Control‐Impact framework based on longitudinal data from 168 respondents. To account for the fact that respondents volunteered to be exposed to the intervention, in this case the radio show, we used a matching algorithm together with regression to ensure that we could build a credible counterfactual group. Our analysis did not uncover any differences in outcomes between the treatment and control groups, and thus no evidence of the intervention achieving its initial goals. One potential causal mechanism that could have led to this outcome is the low audience penetration rate. Fewer than 40% of respondents listened to the show and among those who did, only about 20% listened to five of more episodes. This research highlights the challenges of implementing and evaluating interventions delivered through mass media in developing countries, and the importance of reporting on interventions even when there is no evidence that they achieved their initial goals. Only through robust evaluation of behavior change interventions and the sharing of lessons learned can conservationists successfully tackle complex issues such as the bushmeat trade.
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.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".