Geopolitical Forecasting Skill in Strategic Intelligence
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
Abstract Extending research by the authors on intelligence forecasting, the forecasting skill of 3622 geopolitical forecasts extracted from strategic intelligence reports was examined. The codable subset of forecasts ( N = 2013) was expressed with verbal probabilities (e.g., likely) and translated to numeric probability equivalents. This subset showed very good calibration and discrimination, but also underconfidence. There was no support for the hypothesis that forecasting skill was good mainly because of the general ease of forecasting topics. First, forecasting skill was as good among authoritative key judgments as in the general set. Second, forecasts that were assigned high degrees of certainty, indicative of ease, ( p ≤ 0.05 or p ≥ 0.95) did not discriminate as well as less certain forecasts (0.05 < p < 0.95), and these subsets did not differ in calibration. Sensitivity and benchmarking tests further revealed that if the 1609 uncodable forecasts were all assigned forecast probabilities of .5 (i.e., if all followed a “cautious ignorance” rule), skill characteristics would still show a large effect size improvement over a variety of guesswork strategies. The findings support a cautiously optimistic assessment of forecasting skill in strategic intelligence and indicate that such skill is not primarily attributable to the selection of easy forecasting topics. However, the large proportion of uncodable cases suggests that intelligence forecasts could be improved by avoiding imprecise language that affects not only the codability but also, in all likelihood, the interpretability and indicative value of forecasts for intelligence consumers. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 it