The data privacy / national security balancing paradigm as applied in the USA and Europe: Achieving an acceptable balance
Bibliographic record
Abstract
The overall research question addressed in this thesis is the data privacy/national security balancing paradigm, and the contrasting ways in which this operates in Europe and the U.S. Within this framework, the influences causing the balance to shift in one direction or another are examined: for example, the terrorist attacks on two U.S. cities in 2001 and in various countries in Europe in the opening decade of the new millennium and the revelations by Edward Snowden in 2013 of the details of U.S. mass surveillance practices. \n\nThe thesis is divided into three main parts. The first part deals with European attitudes and practices in relation to the balancing paradigm. The second part deals with U.S. attitudes and practices on the same basis. It deals with the influence of the various branches of Government in determining this balance. In the third part, consideration is given to the contracts and similarities between the U.S. and Europe in relation to the balancing process, and in particular to the factors underlying the contrasts. The conclusion to the thesis gives details of the findings arrived at.
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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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".