The effect of calibration training on the calibration of intelligence analysts' judgments
Bibliographic record
Abstract
Abstract Experts are expected to make well‐calibrated judgments within their field, yet a voluminous literature demonstrates miscalibration in human judgment. Calibration training aimed at improving subsequent calibration performance offers a potential solution. We tested the effect of commercial calibration training on a group of 70 intelligence analysts by comparing the miscalibration and bias of their judgments before and after a commercial training course meant to improve calibration across interval estimation and binary choice tasks. Training significantly improved calibration and bias overall, but this effect was contingent on the task. For interval estimation, analysts were overconfident before training and became better calibrated after training. For the binary choice task, however, analysts were initially underconfident and bias increased in this same direction post‐training. Improvement on the two tasks was also uncorrelated. Taken together, results indicate that the training shifted analyst bias toward less confidence rather than having improved metacognitive monitoring ability.
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How this classification was reachedexpand
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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".