Bibliographic record
Abstract
Public health researchers, policy makers, and practitioners agree that health is the outcome of interactions between biological, behavioral, and social determinants. Nonetheless, institutional patterns of research funding and practice remain obstacles to generating research at and between each of these levels. These practices are embedded in historic assumptions about the nature of reality and how it can best be understood. Current debates over the criteria for evaluating public health research have centered on the applicability of the clinical evidence-based medicine (EBM) model to the field of public health. The EBM hierarchy, which is based on traditional scientific assumptions about causality, is insufficient and potentially harmful as the basis for evaluating research on the determinants of health. Yet those who have put forward a critique of EBM have failed to develop a plausible alternative. Critical realism, based on the philosophy of Roy Bhaskar, may provide a way out of the current stalemate, enabling public health researchers from various disciplines and research paradigms to work together, bringing the full weight of scientific knowledge to bear on increasingly complex and global public health problems.
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 | 0.263 | 0.227 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.006 | 0.092 |
| Scholarly communication | 0.029 | 0.086 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.031 | 0.043 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".