“An Ocean of Noise”: H.E. Reilley and the Making of a Legitimate Social Problem, 1911–45
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.055 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".