Does Internet Use Affect Public Perceptions of Technologies in Livestock Production
Bibliographic record
Abstract
Novel technology applications such as cloning and genetic modification in livestock production have not been widely supported by the public. In this study the relationships between attitudes towards animals, internet use and potential uses of genomics (and vaccination) in beef and pork are examined. The public’s attitudes towards animals, based on an AAS score developed by Herzog et al. (1991) could affect how the public sees the use of genomic technologies in livestock production. Media coverage of technology, including use of the internet, may also play a role in attitudes towards new technologies. Public attitudes might impact acceptance of genomic technologies and influence their adoption by producers, hence influencing societal welfare. Understanding some of the factors influencing attitudes can assist in the development and adoption of technologies. Tobit and multinomial regressions for members of the Canadian public suggest that internet use (for the purposes of searching out information on science and technology) is a positive indicator of higher animal attitudes scores (being more protective of animals) which suggests that internet use has both a negative (indirectly through animal attitudes) and a positive (direct) relationship with the use of genomic technologies in livestock production (through the sign of the variable in the attitude towards genomics equations). Respondents’ individual characteristics such as gender, knowledge of genomics applications prior to survey, income level, etc., are also related to their risk/benefit assessment of this livestock production technology.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".