Near-field Communication Technology: Regulatory and Legal Recommendations for Embracing the NFC Revolution
Bibliographic record
Abstract
Despite its ease and convenience, NFC technology raises a number of privacy issues. Chief among these concerns are the collection, retention, and usage of personally-identifying information contained within NFC-enabled devices by both private and public entities. Within that category, the most pressing privacy issues inherent in the collection and usage of such information relate to real-time tracking or after-the-fact habit profiling and identity theft. As well, privacy issues persist around the means used, if any, to secure and protect that information from unauthorized third parties both at the end-user and systemic database levels.\nIn light of these concerns, it is useful to examine the Japanese approach to NFC technology, both dealing with the physical technology itself, and the system of networks and databases in which that technology operates. Japan provides a strong model for comparison given the country’s long-standing use of NFC technology, and robust privacy-protection laws and industry guidelines which exist in relation to NFC usage. While privacy protection legislation exists in Canada, compliance is lacking and enforcement powers virtually non-existent for privacy commissioners. Amending Canada’s privacy legislation by taking cues from the Japanese model will allow Canada to better anticipate, and embrace, the “e-wallet revolution”.
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 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 | 0.000 |
| 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".