Complementaries and Contradictions: National Security and Privacy Risks in U.S. Federal Policy, 1968–2018
Bibliographic record
Abstract
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.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".