Playing with surveillance: The design of a mock RFID-based identification infrastructure for public engagement
Bibliographic record
Abstract
In many jurisdictions around the globe, governments are developing ID schemes based on radio frequency identification (RFID) and biometric technologies. In Canada, four provinces recently implemented RFID based ‘enhanced’ drivers licences (EDL) in response to the United States’ Western Hemisphere Travel Initiative (WHTI), which requires all persons entering the United States to present a valid passport or alternative ‘secure’ document to prove their identity and citizenship. As researchers, we were closely involved in following the EDL policy development process. It became evident, as we attended legislative hearings, that parliamentarians needed clarifications to understand how the RFID identification scheme would function in practice. This project began with the goal of designing prototypes to demonstrate security and civil liberty concerns with a new RFID-based identification (ID) scheme in Canada. Influenced by participatory design and probe approaches to technology design, we built and tested mock infrastructures of RFID-based identification systems including low fidelity paper prototypes, and high fidelity prototypes using RFID-chipped cards, a database, antenna and reader. We also worked closely with civil society organizations to run public engagement activities. This paper reports on our attempts to create spaces for ‘playful’ engagement with RFID-based ID scheme technology at a time of ‘serious’ policy deliberations. Designed in the spirit of serious play, our mock ID infrastructures make the security and civil liberties challenges inherent in the proposed combination of ID cards and databases more visible, while demonstrating how such ID schemes work. At this point, we see future promise in the design and use of mock ID infrastructure for public engagement during relevant policy deliberations about ID schemes and databases which contain personal information.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| 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".