At Face Value: Mapping the Controversy of Facial Recognition Technology in Canada and its use by the RCMP
Bibliographic record
Abstract
Until the use of certain technologies become public, they tend to operate with limited controversy, which is the case with Clearview AI. Current literature on data-driven surveillance considers how these technologies violate constitutional rights to privacy and security. I propose to challenge this narrative by asking us to consider how the ontology of facial recognition is grounded within multiple realities. Specifically how different tools render the function of surveillance as contingent, multiple, and context dependent. By mapping out the controversies around facial recognition technology in Canada, this thesis will offer a critical perspective on what security controversies tell us about practices of surveillance and security politics in general. Please consider referencing my interactive timeline alongside this thesis (https://xmind.app/m/pwRKgK). This timeline functions to visualize different aspects of this controversy, while bridging the gap between multiple publics who may be interested in this case outside of the theoretical scope of this thesis.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.041 | 0.029 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".