Individual confidence intervals do not inform decision-makers about the accuracy of risk assessment evaluations.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Some recent articles have proposed that the confidence interval for the predicted outcome of a single case can be used to describe the predictive accuracy of risk assessments (Hart et al. Br J Psychiat 190:60-65, 2007b; Cooke and Michie, Law Hum Behav 2009). Given that the confidence intervals for an individual prediction are very large, Cooke and colleagues have questioned the wisdom of applying recidivism rates estimated from group data to single cases. In this article, we argue that the confidence intervals for the recidivism outcome predicted for a single case will range between zero to one (i.e., be uninformative) when the outcome is dichotomous and the predicted probability is between .05 and .95. This is true by definition and limits the utility of using individual confidence intervals to measure predictive accuracy. Consequently, other quality indicators (many of which are non-quantitative) are needed to determine the accuracy and error of risk evaluations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it