Bibliographic record
Abstract
The 2010 Vancouver Winter Olympics ushered in “the most complex domestic [surveillance] operation ever undertaken in Canada.” 1 Preparations began at least four years prior to ensure the smooth interinstitutional collaboration between the Royal Canadian Mounted Police (Canada’s federal policing arm), municipal police forces, the Canadian Armed Forces, and private security firms, which together became the Vancouver 2010 Integrated Security Unit. These “official” aspects of surveillance for the Vancouver Games were supplemented by legislative changes, the implementation of new bylaws, and city “guidelines” for residents of Vancouver. This chapter traces these initiatives, as represented within media reports, legislature records, and official documentation, and asks: for whom are these practices intended? Through a qualitative research project designed to investigate the experiences of homeless and street-involved youth in the year prior to and during the Vancouver Winter Olympics, the chapter attempts to illuminate the effects of such surveillance regimes on the most marginalized members of host cities. In doing so, I wish to highlight larger issues pertaining to the neoliberalization of city spaces, intensified under the auspices of Olympic preparations, and the implications these have for marginalized residents’ citizenship rights within urban spaces. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".