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Record W1509905651 · doi:10.1017/s2071832200020642

Interest-Balancing vs. Fiduciary Duty: Two Models for National Security Law

2012· article· en· W1509905651 on OpenAlexaff
Evan Fox-Decent, Evan J. Criddle

Bibliographic record

VenueGerman Law Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsMcGill University
Fundersnot available
KeywordsFiduciaryLawDutyPolitical scienceHuman rightsBalance (ability)Public interestTortureNational securityState (computer science)EnforcementLaw and economicsSociology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.039
Scholarly communication0.0130.022
Open science0.0030.007
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.050
GPT teacher head0.364
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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