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Record W2980168724 · doi:10.1017/cls.2019.20

The Law of Bad Smells: Making and Adjudicating Offensiveness Claims in Contemporary Local Law

2019· article· en· W2980168724 on OpenAlexaboutno aff
Mariana Valverde

Bibliographic record

VenueCanadian Journal of Law and Society / Revue Canadienne Droit et Société · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsAdjudicationOffensiveComplaintLawPolitical scienceTrademarkBusinessEngineeringOperations research

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.273
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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