Zoonoses Transfer, Factory Farms and Unsustainable Human–Animal Relations
Bibliographic record
Abstract
Infectious diseases are rooted in unsustainable and unjust human–animal relationships. Zoonoses are facilitated by human proximity to animals, epidemiological risk embedded within factory farms, and exploitation of animals and humans in these intensive livestock production systems. The five major categories of epidemiological risk that factory farms propel include: intensification of production for which homogenous populations are congregated, creation of multi-species farms for which different animals are held within the same farm, long and intensive animal transport increases the likelihood of interaction with other wildlife, ecological characteristics of the pathogen lead to altered pathogen dynamics and antibiotic resistance within a human population through the overuse of antibiotics. Layer and broiler operations in the North American context illustrate these linkages. One Health is offered as a concluding conceptual and aspirational frame for pursuing a more sustainable and just world. This article offers two main messages. First, our relationships with animals directly impact the health of human populations through the transmission and creation of Emerging Infectious Diseases (EIDs). Second, adopting One Health offers a means forward for more just and sustainable human–animal relations and reduction of zoonoses transmission.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".