Systematic Monitoring of 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
Accurate indications about consequential future events that arrive early enough can help decision-makers avert trouble. Unsurprisingly, then, forecasting (or prediction, which I use synonymously) plays a vital role in intelligence assessment. According to Allied intelligence doctrine, “analysis does more than look at the current situation, it should be predictive and therefore should address what might happen next, based upon alternative assumptions regarding the actions and reactions of different actors (including the impact of any intervention)” (Ref. [1], §3.38). An effective forecasting capability supports planning and decision-making at all levels, ranging from tactical to strategic. And, although the empirical research reported in this chapter focuses on efforts to monitor forecasting accuracy at the strategic level of intelligence production, the issues dealt with apply as well to forecasting at the tactical and operational levels. Moreover, the methods described for monitoring forecast accuracy and forecasters’ skill could be applied at those levels as well.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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