On Distinguishing Defence Inputs in an Alliance – The Case of NORAD
Why this work is in the frame
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Bibliographic record
Abstract
Our model extends the joint-products models to allow for two types of defence inputs used to produce both an alliance-wide public defence output and a country-specific private output. Distinguishing different defence inputs is particularly appropriate in the case of the North American Aerospace Defense Command (NORAD), as the alliance-wide defence output is produced with two inputs – military technology in the form of sensors and radars and land. These two inputs are complements in the production of the alliance-wide public output. At the same time, the military technology has country-specific private benefits as this can be used by the civilian economy. Our analysis shows that distinguishing between defence inputs may change the predictions of the joint-products model. We derive conditions under which an ally responds to an increase in the defence input by other allies by increasing or decreasing its own contribution of both or only one of the defence inputs.
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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 | 0.000 | 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 it