Societal views and animal welfare science: understanding why the modified cage may fail and other stories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The innovations developed by scientists working on animal welfare are often not adopted in practice. In this paper, we argue that one important reason for this failure is that the solutions proposed do not adequately address the societal concerns that motivated the original research. Some solutions also fail because they do not adequately address perceived constraints within the industry. Using examples from our own recent work, we show how research methods from the social sciences can address both of these limitations. For example, those who persist in tail-docking cattle (despite an abundance of evidence showing that the practice has no benefits) often justify their position by citing concern for cow cleanliness. This result informs the nature of new extension efforts directed at farmers that continue to tail dock, suggesting that these efforts will be more effective if they focus on providing producers with methods (of proven efficacy) for keeping cows clean. Work on pain mitigation for dehorning shows that some participants reluctant to provide pain relief believe that the pain from this procedure is short lasting and has little impact on the calf. This result informs the direction of new biological research efforts to understand both the magnitude and duration of any suffering that result from this type of procedure. These, and other examples, illustrate how social science methodologies can document the shared and divergent values of different stakeholders (to ensure that proposed solutions align with mainstream values), beliefs regarding the available evidence (to help target new scientific research that meets the perceived gaps), and barriers in implementing changes (to ease adoption of ideas by addressing these barriers).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it