Words Matter: Implications of Semantics and Imagery in Framing Animal-Welfare Issues
Bibliographic record
Abstract
As criticisms of contemporary farm-animal production escalate, scholars have begun to scrutinize the imagery and linguistic techniques used to frame animal issues and their implications. Pro-animal rights groups typically present animal use as unnecessary, oppressive, and exploitive and adopt themes of compassion and protection to engage the public. In contrast, anti-animal rights groups represent animal use as necessary for human benefit and often situate animal and human interests as being incompatible. Overly simplistic, polarized representations of animal issues therefore emerge. Several analyses, however, have indicated that the discourse on farm-animal production fails to either make a compelling ethical argument for animal agriculture or address the ethical concerns raised by animal-rights activists. Proponents of animal agriculture are argued to consistently misrepresent animal production practices and portray animals as inanimate objects, reflecting lack of genuine concern for animal suffering or welfare. Thus far, the veterinary community has escaped this level of scrutiny. However, veterinarians are often viewed as being connected to animal agriculture. As veterinarians strive to assume leadership in animal welfare, it is useful for the profession to recognize that, as is the case for members of the animal sciences and industries, some aspects of its discourse may contradict its professed values and beliefs about animal care and welfare. Analysis of this discourse affords the opportunity to more effectively engage with the public on animal-welfare issues and to develop a compelling narrative of the role of animals in an increasingly urban society.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".