Surveilling Modern Surveillance: An Examination of the Impacts of Surveillance on Marginalized Identities and Police Behaviour
Bibliographic record
Abstract
The meaning of public and private space in Canada is increasingly being controlled and defined technocratically along identity-based lines. In view of this reality, the rise of state-led and state-sponsored surveillance techniques, tools and modalities has real social justice implications; and this is particularly true within the context of identity and social location. Socialized meaning(s) created by the utilization and interpretation - of surveillance technologies are often highly dependant on the identity of who is being surveilled. This means that gendered, minority and othered identities can experience the meaning(s) of surveillance very differently. Moreover, the proliferation of techno-surveillance also impacts how already-marginalized identities are perceived and presented within the overall public discourse. \nThe objective of this study is to better understand how these considerations play-out in real world scenarios and within different Canadian space(s) every day. By utilizing previous work done on concepts like Visibility (Brighenti), Racialized Surveillance (Fiske) and gendered surveillance (Glasbeek), the study will examine the ways in which state-controlled surveillance serves as a tool that marginalizes and disempowers particular groups who are already in positions of social and economic disadvantage. \nThe study focuses on particular techno-surveillance tools; and the increasing use of policy-worn body cameras and closed-circuit video monitoring will be examined as a type of visibility mediator capable of shaping public perception and altering identity discourses. This type of state-led surveillance will also be examined vis--vis the context of public push-back and user-created counter-surveillance (most commonly in the form of cell-phone videos) as a (moderating?) influence, as well as the implications these developments have on the so-called de-policing or Ferguson effect. \n \nKeywords: Surveillance, Police-worn body cameras, Identity, Closed-circuit surveillance, Visibility, De-policing.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".