Attunement Beyond Nuisance: Olfactory Techniques of Power, Regimes of Perceptibility, and the Permission to Pollute
Bibliographic record
Abstract
This paper documents a research-creation project exploring the olfactory dimensions of the public perceptions of air pollution, via a case study of the public and media response in Montreal’s Plateau-Mile End neighbourhood, to wood smoke, and subsequent municipal legislation against wood-burning. It employs the concept of ‘nuisance’ through the creation of an augmented reality media game and other methods to explore the perceptual tensions between annoyance and injury, sensory and physical harm, and the power dynamics that render harms publicly legible or not within larger ‘regimes of perceptibility.’ Within this, the thesis explores how perceived environmental threats to bodily integrity can coalesce a ‘visceral public’, and specifically how odour impacts how such threats are perceived and responded to. The visceral response is contextualised within histories of smoke, smell and public health, studies of public risk perception of air pollution, and the implications of slow violence and uneven geographic distribution of harm from air pollution. Using the Situational Analysis-informed methodology of analytic abduction, the research-creation methods were messy mapping and smellwalking, all of which informed the creation of the interactive augmented reality web game, Nuisances. Through Nuisances, the olfactory ‘techniques of power’ that uphold permission to pollute are identified as Disgust, Diffusion and Differentiation. The game seeks to address these techniques by proposing to offer a the public an alternative mode of perceiving pollution, one that withdraws permission to pollute. Employing the concept of ‘attuned sensing’ in an augmented game experience, Nuisances encourages players to attune to both the sensory dimensions of smell and the broader situation to counter the techniques of power, and produce its own counter-regime of perception.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".