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Record W2901040622 · doi:10.1002/poi3.189

Complementaries and Contradictions: National Security and Privacy Risks in U.S. Federal Policy, 1968–2018

2018· article· en· W2901040622 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePolicy & Internet · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
FundersMinistry of Science and Technology, IsraelAzrieli FoundationIsrael Science Foundation
KeywordsCompromiseNational securityHarmTransparency (behavior)Context (archaeology)Policy analysisSecurity policyPolitical sciencePublic administrationComputer securityLawComputer science

Abstract

fetched live from OpenAlex

How does the U.S. balance privacy with national security? This article analyzes how the three regulatory regimes of information collection for criminal investigations, foreign intelligence gathering, and cybersecurity have balanced privacy with national security over a 50‐year period. A longitudinal, arena‐based analysis is conducted of policies (N = 63) introduced between 1968 and 2018 to determine how policy processes harm, compromise, or complement privacy and national security. The study considers the roles of context, process, actor variance, and commercial interests in these policy constructions. Analysis over time reveals that policy actors’ instrumental use of technological contexts and invocations of security crises and privacy scandals have influenced policy changes. Analysis across policy arenas shows that actor variance and levels of transparency in the process shape policy outcomes and highlights the conflicting roles of commercial interests in favor of and in opposition to privacy safeguards. While the existing literature does address these relationships, it mostly focuses on one of the three regulatory regimes over a limited period. Considering these regimes together, the article uses a comparative process‐tracing analysis to show how and explain why policy processes dynamically construct different kinds of relationships across time and space.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.113
GPT teacher head0.439
Teacher spread0.326 · 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