Public perception of animal welfare in Iran
Bibliographic record
Abstract
While animal welfare is a growing global concern, there has been very little research into how it is understood in Iran. Cultural, religious, and legal factors influence attitudes and practices in ways not addressed by existing research. This study provides culturally grounded insights for improvement of animal welfare in Iran. Utilising a validated survey tool, we investigated the attitudes of Iranians toward the welfare of farmed, companion, and wild animals. A total of 325 responses were collected. The findings indicate that animal welfare is considered important to Iranians, with the majority expressing interest in improving the welfare practices. Despite varying degrees of familiarity with different animal species, there was a consensus on the importance of enacting laws to protect animal welfare. Most participants agreed that chickens feel pain (92.9%) and emotions (79%), whereas fewer attributed these capacities to fish, with 63.6% acknowledging pain and 59.5% acknowledging emotions. Furthermore, most of the participants agreed that animals should not endure pain in the slaughter process (97.8% agreement). While the majority of participants agreed that pre-slaughter stunning was better for the animals (78.7%), only 51.7% agreed that they would prefer to eat meat from animals that had been stunned; reflecting the traditionally held views regarding the role of stunning in Halal meat production. The results of the current study support previous findings suggesting that concern for animals may be a universal human inclination, although, in Iran, attitudes towards specific species and agricultural practices are also shaped by religious perspectives.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".