The Haunting of Surveillance Studies: Seeing, Knowing, and Ghostly Apparitions
Bibliographic record
Abstract
This paper explores the relevance of a ghost methodology for surveillance studies. Following Torin Monahan’s (2021) call to unsettle transparency as a metric or goal of surveillance studies and inspired by Michelle Brown’s (2022) demand that criminology exorcise the ghosts of white supremacy, I draw upon a 2020 case of a police-involved death of a racialized woman in Toronto to consider the haunting absence of images that are usually called upon to offer evidence what “really” happened. Against the desire to make this death empirically knowable, a ghost method asks us to live with the “eerie” remnants of violence as palpable presences that require of us a reckoning. The spectral presence of white supremacy that looms over the ghostly absence of Regis Korchinski-Paquet can lead us to a form of redress consistent with abolitionist ways of seeing. Thus, I seek to break the impasse of debates over the costs or benefits of increased transparency by outlining how a ghost methodology can help to decentre surveillance studies’ preoccupation with visibility in favour of a more nuanced appreciation of haunting and absence.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.016 | 0.108 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".