FREEDOM AND THE WAR ON TERROR IN THE DIGITAL AGE
Bibliographic record
Abstract
Advances in Computer Science continue to provide more tools, each time more efficient, to aid us in our everyday lives with everything from work to entertainment, from health to management of natural resources. Technology has made our lives better and continues to facilitate progress. But just as it can benefit us, it is only a tool. That tool itself is not ‘good ’ or ‘evil’; it is only useful to achieve the ends of those who utilize it. And just as it has been used for benefit, there have been cases where its use had a detrimental impact to us. Given the benefits, who is to deny that a government can keep track of its citizens in the same way a business keeps track of all its assets? The discussion here will centre on this question, where we suspect that, given technological advances, governments are tempted to achieve this goal. The purpose is to present some of the policies that governments in North America and Europe have proposed and/or adopted with respect to technology and national security, and point out flaws that could allow the undue erosion of privacy and free speech in the electronic world as a consequence. We reflect upon those measures that seem unjustified and unnecessary even in the face of terrorism, and argue that none of them include adequate safeguards to minimize the risk of abuse. We hope the reader will realize that none of the measures discussed admit that technology can accommodate the protection of civil liberties as well as security. We also argue that at least in Canada’s case, not counting academia and civil rights groups, policies and laws introduced as a consequence of the events of 9/11 seem to receive little attention from the public at large. Citizens would appear to be unaware of what is being done to mitigate the terrorist threat. Indeed, amidst such legislative actions, we may be getting used to living in a permanent state of war. We hope our conclusions give an insight into the current landscape of privacy protection in ii North America and, to some extent, Europe.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".