Bibliographic record
Abstract
This work examines the philosophical foundations of information ethics and their potential for application to contemporary problems in U.S. foreign intelligence surveillance. Questions concerning the limits of government intrusion on protected Fourth Amendment rights are examined against the backdrop of the post-9/11 period. Changes to U.S. foreign intelligence surveillance law and policy are analyzed by applying the traditional ethical theories commonly used to support or discount these changes, namely utilitarian and contractarian ethical theories. The resulting research combines both theoretical elements, through its use of analytic philosophy, and qualitative research methods, through its use of legislation, court cases, news media, and scholarship surrounding U.S. foreign intelligence surveillance. Using the U.S.A. PATRIOT Act, the Foreign Intelligence Surveillance Act (FISA) and the Terrorist Surveillance Program as case examples, the author develops and applies a normative ethical framework based on a legal proportionality test that can be applied to future cases involving U.S. foreign intelligence surveillance. The proportionality test developed in this research, which is based on a modified version of the Canadian Oakes Test, seeks to balance legitimate concerns about collective security against the rights of the individual. As a new synthesis of utilitarian and contractarian ethical principles, the proportionality test laid out in this book has potential for application beyond U.S. foreign intelligence surveillance. It could act as a guide to future research in other applied areas in information policy research where there is a clear tension between individual civil liberties and the collective good of society. Problems such as passenger screening, racial and ethnic profiling, data mining, and access to information could be examined using the framework developed in this study.
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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".