Butchers’ and deli workers’ psychological adaptation to meat.
Bibliographic record
Abstract
In many societies today, the average consumer is largely removed from the earlier stages of meat production wherein meat, in many ways, resembles an animal. The present study examined the emotional and psychological consequences of recurrent meat handling. Fifty-six individuals with commercial experience handling meat (butchers and deli workers) were contrasted with 103 individuals without such experience. Participants were presented images of meat from 3 animals-cows, sheep, and fish-that were experimentally manipulated in their degree of animal resemblance. Participants rated the images on measures of disgust, empathy for the animal, and meat-animal association. Broader beliefs and attitudes about meat and animals were also assessed. We used mixed-effect linear modeling to examine the role of time spent handling meat in participants' psychological adaptation to it. We observed significant reductions in disgust, empathy, and meat-animal association within the first year or 2 of meat handling for all types of meat. Time spent handling meat also predicted the degree to which a person defended and rationalized meat consumption and production, independent of a participant's gender and age. The findings have implications for understanding how people adapt to potentially aversive contexts such as handling animal parts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".