The Law of Bad Smells: Making and Adjudicating Offensiveness Claims in Contemporary Local Law
Bibliographic record
Abstract
Abstract The sense of smell seems to have resisted the kind of objective measurement process that might have facilitated settling competing claims about offensive smells by applying a general rule or standard. As a result, authorities,including courts,cannot avoid making subjective judgements of taste. A nuisance lawsuit out of Ontario regarding a mushroom farm, or rather its smells, is used here as one source of material about the difficulties of adjudication in this subfield of the “law of the senses.” Attention is also paid to a curious quasi-judicial entity, Ontario’s Normal Farm Practices Protection Board, charged with resolving, mainly through mediation, disputes about farm smells between farmers and non-farming neighbours. Overall, the article shows that the ex post facto, situated and complaint-driven logic of nuisance that nineteenth-century law used to govern offensive noises as well as nasty smells, and which left plenty of room for subjective judgements of taste, keeps reappearing in the present day. Nasty smells seem particularly impervious to modernization, that is to being managed through objective measurement and preventive regulation.
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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.088 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 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".