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 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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.038 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".