Pinpointing Consent: Location Privacy, Public Safety, and Mobile Phones
Bibliographic record
Abstract
Accompanying the growing interest in emerging location based services has been an urgent but rather ambiguous concern shared among scholars, privacy advocates and the general public that enhanced safety will come at the expense of personal privacy. Precision tracking of mobile devices has raised the spectre of unwanted and pervasive surveillance from both state and commercial interests. This juxtaposition of public safety with fears over loss of privacy raises important questions about appropriate collection, use, and disclosure of personal information in the context of mobile phone services and network-enabled devices in general. This paper will report on findings from an ongoing study that is looking at initiatives to introduce location-based services for public safety in the United States, Canada, and Europe. It will address a number of aspects of these developments by drawing on official regulatory and policy sources. In particular, this paper will examine the various legal and regulatory aspects related to the conditions by which “consent ” is established for collection, use, and disclosure of location information generated by mobile phones and other mobile access technologies.
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 | 0.055 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.076 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.012 |
| 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".