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Record W2776415336 · doi:10.3138/jcs.2016-0001.r1

“An Ocean of Noise”: H.E. Reilley and the Making of a Legitimate Social Problem, 1911–45

2017· article· en· W2776415336 on OpenAlexvenueaboutno aff
Jarrett Rudy, Nicolas Kenny, Magda Fahrni

Bibliographic record

VenueJournal of Canadian Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyModernityNoise (video)ReflexivityLegitimacyLawMedia studiesPolitical scienceSocial scienceComputer sciencePolitics

Abstract

fetched live from OpenAlex

In the 1930s and early 1940s, McGill University physics professor H.E. Reilley led Montreal efforts to limit noise, evaluating the acoustics in buildings, giving speeches on the dangers of noise, and playing a part in writing the city’s first comprehensive anti-noise bylaw. Situating his noise abatement movement within the international rise of the field of applied acoustics, the advent of anti-noise campaigns in other countries, and previous state efforts to limit noise in Montreal, this article explores the numerous ways Reilley sought to make the dangers of noise a legitimate social issue in a culturally divided city. We argue that Reilley used his affiliation with McGill to overcome problems of professional legitimacy and that he sought powerful allies for his cause, deliberately bridging the French-English divide inherent to this industrial city. Reilley achieved less success in extending legal limitations on noise into the workplace and in having noise bylaws enforced. Ultimately, Reilley’s noise abatement campaigns represent particularly strong examples of reflexive modernity—a specifically modern response to problems brought about by modernity itself.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.068
GPT teacher head0.312
Teacher spread0.244 · 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 designNot applicable
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

Citations2
Published2017
Admission routes2
Has abstractyes

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