From the Editors: Communicating animal science to the public, policymakers, and students
Bibliographic record
Abstract
This issue of Animal Frontiers, “Communicating the Animal Sciences Effectively,” focuses on the challenges associated with how best to communicate animal science information with policymakers, the public, and students. Communicating effectively with these stakeholder groups is critical to the future of the livestock and poultry industries. In addition, the general public gets most of its information related to food systems from the internet, family, or friends, and these sources often contain inaccurate information. It is not surprising, then, that communication about the production of meat, milk, and eggs has become increasingly difficult. The article by Johnson and Hamernik (2015) explores the reasons effective communication is important for how the public makes informed decisions. Scientists often try to educate the public with the hope that a better understanding of scientific and technical facts will enable the public to view controversial issues from the same perspective as a scientist. However, the public is also interested in the social, ethical, and economic aspects of issues. Effective communication on issues related to the management of livestock and poultry will require a commitment to building trust, shared values, ethics, and credible expertise. Glenn et al. (2015) describe the importance of sound agricultural and livestock policies in the United States to allow opportunities for animal science research, education, and extension activities that will be necessary to meet the growing demand for animal-sourced foods. Although most American politicians do not understand science, the unified voice of thousands of animal scientists as advocates for federal funding for animal science research or research friendly policies and regulations can have a tremendous, positive impact on elected officials. Federal policies and regulations in the United States are made based on emotions, trust, scientific facts, and clear and concise communication. Fraser (2015) provides a historical perspective of how science influenced Canadian policy for the welfare of farm animals. Development of national, science-based standards for farm animal welfare in Canada resulted from the timely actions of a number of stakeholder groups, including livestock producers, national producer organizations, retailers, and scientists. Each of these groups provided trusted individuals to participate in conversations and engage in debates on controversial issues associated with the welfare of farm animals. Social media, the internet, and conversations with the public and policymakers can be used to provide more information about the production of meat, milk, and eggs. This information can enhance a stakeholder’s ability to make informed decisions and to refute misinformed communications
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".