How the Sausage Is Made: Testing the Effectiveness of an Informative Video in Promoting Sustainable Food Consumption
Bibliographic record
Abstract
We use an incentivized consumption measure to assess the impacts of watching a roughly 15-minute video about industrial-scale pork production on sustainable food consumption. We find that the demand for (more sustainable) plant-based meals increases by 252.7 percent while the demand for meat-containing meals decreases by 27.0 percent immediately after watching the video. Notably, these effects exhibit some persistence. Compared to baseline levels, we find that demand at a one-week follow-up is still 121.8 percent higher for plant-based meals and 13.5 percent lower for meat-containing meals. The video appears to have a particularly large impact on the demand for pork, with immediate and one-week decreases of 45.2 and 30.1 percent. We also observe smaller yet still significant decreases in the demand for other meat products, with immediate and one-week reductions of 24.0 and 10.6 percent, suggesting that information about industrial-scale pork production can affect preferences for other types of meat. Overall, the video appears to be quite effective in promoting plant-based eating compared to others tested in the literature. Even so, the relatively low baseline popularity of pork compared to other meats in our sample suggests that the impact of the video may be limited by its focus on pork, raising the possibility that a broader portrayal of industrial meat production could be even more effective in promoting sustainable food consumption.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".