Tracking Accuracy of Strategic Intelligence Forecasts: Findings from a Long-Term Canadian Study
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
Forecasting plays a vital role in intelligence assessment and contributes to national security decision-making by improving strategic foresight. Remarkably, most intelligence organizations do not proactively track their forecasting accuracy and, therefore, do not know how accurate their forecasts are or what types of biases intelligence analysts (or organizations) might exhibit. We review research on geopolitical forecasting and a roughly decade-long program of research to assess the accuracy of strategic intelligence forecasts produced by and for the Government of Canada. This research is described in three phases corresponding to previously published research, following which novel analyses (drawing from the data used in the earlier phases) are reported. The findings reveal a high degree of forecasting accuracy as well as significant underconfidence. These results were evident regardless of whether analysts assigned numeric probabilities to their forecasts. However, the novel analyses clarified that there is a substantial cost to accuracy if end-users rely on their own interpretations of verbal probability terms used in the forecasts. We recommend that intelligence organizations proactively track forecasting accuracy as a means of supporting accountability and organizational learning. We also recommend that intelligence organizations use numeric probabilities in forecasts as a means of improving intelligence producer-consumer agreement in the interpretation of forecasts and other probabilistic assessments.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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