CONNECTING LIVES: REFLECTIONS ON A SYNDEMIC APPROACH TO PREVENTION INVOLVING RESEARCH ON HOW PEOPLE RELATE TO PETS
Bibliographic record
Abstract
This article elaborates on what could be meant by a syndemic approach to prevention. The main argument is that a syndemic approach to prevention comprises a set of practices intended to promote population health. These practices include research, team‐building, and developing partnerships with organizations with diverse mandates. Both infectious and noninfectious diseases may be targeted, in animal as well as human populations. By way of illustration, a research program on urban pets and population health is presented. While this research program has become highly interdisciplinary, theories and practices rooted in anthropology have driven its development.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.096 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".