Machine Learning in Public Health and the Prediction Intervention Gap
Bibliographic record
Abstract
This chapter examines the epistemic value of (purely) predictive ML models for public health. By discussing a novel strand of research at the intersection of ML and economics that recasts policy problems as prediction problems, we argue – against skeptics – that predictive models can indeed be a useful guide for policy interventions, provided that certain conditions hold. Using behavioral approaches to policymaking such as Nudge theory as a contrast class, we carve out a distinct feature of the ML approach to public policy problems: the ML model itself may turn into a cognitive intervention. In underscoring the epistemic value of predictive models, we also highlight the importance of taking a broader perspective on what constitutes good evidence for policymaking. Moreover, by focusing on public health, we also contribute to the understanding of the specific methodological challenges of ML-driven science outside of traditional success areas.
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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 | 0.049 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".