Interest-Balancing vs. Fiduciary Duty: Two Models for National Security Law
Bibliographic record
Abstract
The metaphor of the balance has long dominated national security law and policy. As Richard Posner has explained, one side of the balance “contains individual rights, the other community safety, with the balance needing and receiving readjustment from time to time as the weights of the respective interests change. The safer we feel, the more weight we place on the interest in personal liberty; the more endangered we feel, the more weight we place on the interest in safety, while recognizing the interdependence of the two interests.” While policymakers have debated the relative weight states should give to civil liberty concerns and public security concerns in various contexts, few question the general balance metaphor that structures these debates. By all accounts, interest balancing has provided the primary model for making national security law and policy worldwide since September 11, 2001.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".