Bridging The Social-Biomedical Divide: Uncovering Explanatory Conflicts In The Public Health Literature
Bibliographic record
Abstract
Purpose and Research Objective: Philosophers of science have paid significant attention to monism, the conviction that there is a single salient explanation for a given phenomenon in the natural world. Since this view can cause research programs to ignore or discredit alternative scientific understandings, it presents a barrier to interdisciplinary research and intellectual plurality. To date, no study has sought to systematically characterize monistic conflicts in public health research, specifically disagreements between the social determinants focused “Social approaches” and the idiosyncratic “Biomedical approaches”. This qualitative study seeks to fill this gap in the literature by uncovering instances of monistic conflict between the social and biomedical approaches in the public health literature, utilizing childhood obesity as a case study. Methods: The project is a narrative literature review of review articles on childhood obesity in North America. Researchers will use qualitative content analysis to examine the articles found. Results: Completion of the literature search revealed a bias toward the “biomedical approach”, with more articles focusing on the medical and behavioral explanations of childhood obesity issues in North America. The content analysis of the articles revealed monistic thought within the social and biomedical approaches. Monism most often took the form of omission, whith approaches neglecting to mention the causal factors central to the other approach in their explanations. Monism also appeared in the repuposing of language in biomedical articles, in which social approach terms were used in conjunction with biomedical explanations, changing their meaning in context. Implications: Monism is a barrier to interdisciplinary research, making it a phenomenon of interest to the field of public health, which strives for multifaceted health solutions. Understanding of the nature and extent of monism in the discipline can serve as a first step to eliminating such barriers.
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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 | 0.145 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.014 | 0.057 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.005 | 0.007 |
| 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".