Bibliographic record
Abstract
Location based services (LBS) potentially put the privacy of individuals at risk. The increased possibility to know people’s whereabouts is posing the question of possibility versus desirability with regard to location privacy. The central question that this article aims to answer is how location privacy needs of cell phone users may be balanced with national security needs of society? Through a study of literature and rulings of the European Court of Human Rights a balancing framework was developed. The framework allowed for the assessment of the situation in the Netherlands, Germany and Canada with respect to the location data from mobile devices used by intelligence and security agencies to protect the national security. The research shows that the balancing should account for the totality of the circumstances. A true balancing should be accomplished on a case-by-case basis. It is not a priori to be determined whether and to what extent location privacy is at stake. A proper balancing strongly builds on the balancing process, especially when balancing is very context-sensitive. This process should be just with adequate safeguards against abuse.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".