Bibliographic record
Abstract
Renewed concerns about information privacy and mobile phones have surfaced with the early deployment of location-based services in North America, and specifically with the Federal Communications Commission (FCC) led public safety initiative known as Wireless E9-1-1. Initial scholarly research in this area has focussed on the use and disclosure of geographic location information of mobile phone subscribers and on the terms and conditions by which this information can be made available for lawful access or commercial purposes. This paper refers to this body of research as the 'first domain' of information privacy research, and describes some of the key findings and contributions for policy research on customer proprietary information and customer consent. The paper then turns to introduce and describe an emerging 'second domain' of information privacy concerned with the popular adoption of anonymous prepaid mobile phone services. The distinguishing characteristic of this second domain of research is its focus on debates about the legitimacy of regulatory requirements to collect and verify customer details at the point of purchase. This paper draws on findings from an empirical study undertaken in Canada to identify some initial parameters of this second domain of information privacy research with the intent of informing a wider debate about the entitlement to anonymity for customers who elect to use prepaid services over commercial networks.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".