Effect of confidence interval construction on judgment accuracy
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Three experiments ( N = 550) examined the effect of an interval construction elicitation method used in several expert elicitation studies on judgment accuracy. Participants made judgments about topics that were either searchable or unsearchable online using one of two order variations of the interval construction procedure. One group of participants provided their best judgment (one step) prior to constructing an interval (i.e., lower bound, upper bound, and a confidence rating that the correct value fell in the range provided), whereas another group of participants provided their best judgment last, after the three-step confidence interval was constructed. The overall effect of this elicitation method was not significant in 8 out of 9 univariate tests. Moreover, the calibration of confidence intervals was not affected by elicitation order. The findings warrant skepticism regarding the benefit of prior confidence interval construction for improving judgment accuracy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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