Accuracy of Intelligence Forecasts From the Intelligence Consumer’s Perspective
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 forecasting is a vital part of intelligence assessment. Only recently has intelligence forecast accuracy been quantitatively tracked. Mandel and Barnes reported on a long-term study of intelligence forecasts that examined accuracy from the analysts’ perspective using numerical probabilities that were not reported to intelligence consumers. The present research reassessed the accuracy of those forecasts from an intelligence consumer’s perspective using findings from an experiment that elicited from subjects’ numerical probability equivalents for the linguistic probabilities that consumers would have read in intelligence reports. Forecast accuracy was undiminished when assessed from the consumers’ perspective (inferred from subjects’ median numerical equivalents) because the intended meaning of the probability terms used by the intelligence unit corresponded well to the average meaning assigned by subjects. The findings also showed that interpretations of linguistic probabilities are context-dependent. Linguistic probabilities were discriminated better when applied to outcomes that represented successes rather than failures.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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