The “Right” to Privacy? - The Debate over the United States Government’s Control over its Cyberspace
Bibliographic record
Abstract
This research note centres on a critical issue in the United States, that is, in the name of anti-terrorism the state utilises sophisticated cyber-surveillance machinery to protect its citizens" while at the same time promising to protect their civil liberties. The examination here of this issue seeks to go beyond the dichotomy of "liberty vs. security" but to how open the United States government should be about its cyberintelligence capabilities and how these apparatuses are possibly infringing upon its citizens" freedoms. The freshness of the controversy stems from Edward Snowden"s claims the U.S. government (and its Allies) acted criminally by aiding and abetting its own agents to collect information on its populace in the absence of lawful means. This case has brought a range of legal and ethical questions on democratic states" cyber-intelligence gathering including calls for legislation to directly deal with 3 key issues: reduced privacy, increased government secrecy, strengthened government protection of special interests. This research will further discuss the demands of cyber-intelligence reforms put forth by Edward Snowden and whether these demands are in fact practical in modern, high-technology societies such as the United States.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".