Bibliographic record
Abstract
Agent, host and environmental factors offer a powerful approach to elucidating the causes of an outbreak of infectious disease after it occurs. They are less helpful in predicting when an outbreak will occur and do not explain why the conditions for the outbreak arose in the first place. As public health turns its attention toward non-infectious health problems such as obesity or adolescent suicide, other limitations of this traditional approach become clear. The agents involved in non-infectious diseases are usually non-specific and there may be no necessary causal factors; host susceptibility cannot be measured in terms of an immunological test, and the environment takes the form of complex, interacting layers of influence. In such situations, causal explanations in terms of agent, host and environment show significant limitations. This discussion reviews alternative approaches to thinking about public health problems and discusses the connections among causes, explanations and understanding. Epidemiologists who study health issues such as obesity or suicide refer to risk factors for the conditions, in tacit admission that there are no specific causal agents. Population health researchers speak in terms of health determinants, which include a broad swath of factors from genetics to social inequities. In each case, the precise causal status of such factors is frequently left unclear; the published lists of odds ratios provide little in the way of an overall explanation. Interventions should be based on coherent explanations, and the lack of progress in resolving these problems suggests that our explanations are lacking.
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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".