Internet of Torment: The Governance of Smart Home Technologies Against Technology-Facilitated Violence
Bibliographic record
Abstract
While smart home technologies (SHTs) are often marketed as solutions to automate household activities, they can be used to cause harm. This thesis examines the emergence of smart home technology-facilitated violence (smart home TFV) in Canada to assess 1) how these Internet of Things (IoT) devices can be misused as technologies of torment and 2) how and why we may want to examine this phenomenon as part of a wider sociotechnical system of human and non-human actors. Using a modified Walkthrough Method (Light, Burgess and Duguay, 2018) and a hybrid theoretical framework of assemblage theory (Kitchin, 2014), actor-network theory (Latour, 1988; Latour, 2005) and multi-scalar analysis (Edwards, 2003), this approach illuminates what may initially be misconstrued as an isolated, one-on-one dispute as a practice that is enabled, mitigated, and ultimately shaped by a variety of contexts and interactions with other components within a complex and heterogeneous sociotechnical assemblage (Kitchin, 2014).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".