Oriented data-generating processes: a categorization of ROC curves
Bibliographic record
Abstract
Decision makers attempting to classify a binary state of the world may commit two types of errors. Even when the two alternative states have equal prior probabilities and when the two types of errors are equally costly, a classification criterion may be chosen which leads to one type of error being committed more frequently than the other, because of asymmetries in the data that informs their decisions. We formalize this possibility through a categorization of data-generating processes (DGPs), which may be ‘oriented’ towards evidence favoring one of the two alternatives, or which may be ‘unoriented’. We identify the shape properties of the receiver operating characteristic (ROC) curves associated with DGPs in these three categories. We also identify the orientation of DGPs obtained from common distribution families. Then, we illustrate the usefulness of our categorization with several applications, e.g., the standard decision making problem, ranking intersecting ROC curves for particular classes of decision makers, interpreting Bayesian persuasion strategies, and burden of proof assignments in simple litigation settings.
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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 | 0.043 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".