Invited review: Qualitative social and human science research focusing on actors in and around dairy farming
Bibliographic record
Abstract
Qualitative research related to humans, dairy cows, calves, and farming has been published by scientists from a variety of disciplines in many journals targeting dairy science audiences. We aimed to investigate how scientific communities other than those working in dairy science describe, analyze, and discuss dairy farming, because we found it important to bring this research to the attention of dairy scientists. In total, 117 articles were identified as involving one or more qualitative research methods in relation to dairy cattle. The review brought out a wealth of perspectives, new insights, and discussions related to dairy cattle, farmers, farming, and the sector, and in relation to societal issues and food and ecological landscapes. A broad range of qualitative research methods were used, and the literature targeted issues at the animal, farm, societal, food system, and landscape levels. Some raised critical questions about existing structures, highlighted unfairness in the industry, or pointed to new potential futures and contemporary agendas. We expect that it will be inspirational and stimulating for researchers to review new sources of literature and suggest a closer interdisciplinary collaboration among researchers from different disciplines for the future development of research involving dairy cattle. Further, it could be relevant and even necessary to engage in such interaction to avoid increasing polarization around future development of the sector-for example, related to climate change or how industrialization seems to push inequity or ignore the agency of animals themselves. Exploring perspectives of farming from different angles could enrich the outcomes of future dairy research.
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 | 0.036 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".