Attitudes Towards Chickens & Fishes: A Study Of Brazil, Canada, China, & India
Bibliographic record
Abstract
Across the world, advocates are working to improve the welfare of animals and to reduce the consumption of animal products. A key front in this work is addressing the consumption of small-bodied animals — namely chickens and fishes — as they are consumed in the highest numbers, by several orders of magnitude. Reducing the consumption of chickens and fishes could result in billions of individuals being saved, and achieving that goal requires us to understand how consumers think of them.\nMany of the countries we have surveyed in this line of research—which includes Brazil, Canada, China, India, and the United States—contribute in huge quantities to the enormous suffering of chickens and fishes. For example, China, the United States, and Brazil slaughtered more chickens than any other countries in 2018, with India not far behind. In terms of tons of fishes slaughtered, China ranked first in the world, while India was fourth and the U.S. was sixth. In total, the five countries considered in this research account for over 40% of the global chicken slaughter and more than a quarter of global fish slaughter.\nBecause of cultural differences across different regions, it is important that advocates understand the context in which they are working rather than assuming that lessons from one part of the world can be applied to audiences in another. Despite the massive quantities of chicken and fish slaughter committed by each of these countries, it is not necessarily the case that their residents share similar beliefs about these animals. By comparing the country-level findings of this study, we can observe similarities and differences in beliefs across countries. This information may be helpful for animal advocates working in their respective national contexts, or in an international context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".