Purposes and duties in scientific modelling
Bibliographic record
Abstract
More people than ever are paying attention to philosophical questions about epidemiological models, including their susceptibility to the influence of social and ethical values, sufficiency to inform policy decisions under certain conditions, and even their fundamental nature. One important question pertains to the purposes of epidemiological models, for example, are COVID-19 models for 'prediction' or 'projection'? Are they adequate for making causal inferences? Is one of their goals, or virtues, to change individual responses to the pandemic? In this essay, we offer our perspective on these questions and place them in the context of other recent philosophical arguments about epidemiological models. We argue that clarifying the intended purpose of a model, and assessing its adequacy for that purpose, are moral-epistemic duties, responsibilities which pertain to knowledge but have moral significance nonetheless. This moral significance, we argue, stems from the inherent value-ladenness of models, along with the potential for models to be used in political decision making in ways that conflict with liberal values and which could lead to downstream harms. Increasing conversation about the moral significance of modelling, we argue, could help us to resist further eroding our standards of democratic scrutiny in the COVID-19 era.
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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 | 0.098 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.099 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".