Gendered knowledge practices and human-animal-ecological health in Papua New Guinea: a qualitative study during a zoonotic tuberculosis investigation
Bibliographic record
Abstract
There have been consistent calls to incorporate gender frameworks into One Health research and policy. Men's and women's roles in relation to animals in many rural and agricultural communities may influence potential pathogen exposure and transmission patterns of zoonotic infectious diseases. However, men's and women's roles related to animals in rural societies are not static but change over time. This article draws on the results of qualitative research about perceptions and practices related to animal species and animal products in five sites located in Eastern Highlands Province and East New Britain, two provinces in Papua New Guinea (PNG). The qualitative research, which aimed to explore animal-human interactions that may pose a risk of zoonotic infections through focus group discussions, formed part of a broader study of zoonotic association and risks for extrapulmonary tuberculosis in PNG. We first introduce participants' descriptions of symptoms of illness that they attributed to zoonotic disease, as well as the animals that they attributed those diseases to. We then draw on an in-depth case study of practices related to rearing pigs - animals with important economic and social value in PNG - to illustrate the impact of social and ecological change on the interplay of gender and relationships with animals. One Health strategies at the community level can focus on gender as a way to understand changes underway in the type and intensity of interactions with animals in rural communities, including their potential impact on zoonotic diseases.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".