Bibliographic record
Abstract
Beliefs form a major component of public attitudes, and attitudes have a role in determining how people behave as consumers and as citizens. Their behavior in turn affects the commercial viability and even the sustainability of animal industries. Beliefs are subjective facts, that is, matters that individuals consider to be true. The process of informing the community necessarily involves changing beliefs and, to this extent, persuasion. Education strategies relating to welfare issues depend on the target group and the desired outcome. Target groups include farmers, post-farm gatekeepers including transport drivers and abattoir workers, carers of companion animals, legislators and regulators, retailers, and the general community. These target groups may not be homogeneous, but each nevertheless has identifiable needs for knowledge and skills relevant to welfare. The approach that is likely to be most effective is to provide appropriately targeted dispassionate and factual information to the community. In this way, when debates about animal welfare occur, all the stakeholders--including animal-rights groups, retailers, farmers, legislators, and regulators--who are involved in the debate are more likely to produce good outcomes if discussion is based on a shared understanding of what current practices are and what science can reveal about welfare. Given that the mass media are the preferred source of information, the use of science-based media coverage and informed ethical debate is likely to have the best effect, albeit over a fairly long time frame.
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 | 0.029 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".