Privacy & Terrorism Review - Where Have We Come in 10 Years?
Bibliographic record
Abstract
As a result of terrorist attacks in the United States on September 11 and subsequent attacks on other influential western countries, new laws have been put in place to supposedly be an effective tool to prevent terrorist attacks and conjointly fight the war on drugs. These laws and presidential executive orders have not been without controversy. The Patriot Act will be used as the primary source of legislation in illustrating how in times of fear governments introduce laws, which normally would not be accepted by the general population as a clear invasion of their privacy. In addition, Canada and the United Kingdom’s anti-terrorist legislations will be compared with the United States. Money laundering, terrorist anti-terrorist finance, government investigative surveillance, and data mining will be the areas this paper will focus on to illustrate the emerging invasion on privacy for the sake of security. Despite the fact that we are losing our privacy to our fears of danger, a light will be shed as to the effectiveness of these new laws. Case law will be used to illustrate that the courts have been reluctant in invalidating laws that infringe our constitutionally given rights of privacy. Possible alternative measures will be given to deal with acts of terrorism. This paper will argue that privacy rights have seen a shift from its traditional understanding since the recent terrorist attacks on the western governments and that security has taken a primary role; privacy rights have been traded as a commodity in the market by the U.S and to a lesser extent the Canadian government.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".