Online commentary on noise concerns, policy, and enforcement among readers in four countries
Bibliographic record
Abstract
Decades’ worth of research findings tell us that most who are affected by noise exposure do not complain about it, and that noise complaints are not an accurate means of measuring community response. Much attention has been given to understanding the motivations of serial complainers, with less given to non-complainers. Are non-complainers unaware of noise policy, or how to submit a noise complaint? Research indicates that this is the case, and also suggests that some people think complaining would be a waste of time, that some are inhibited by social factors, and that some give up when previous efforts to address noise do not succeed. While noise complaints do not accurately measure community response, some policy and enforcement decisions are influenced by noise complaint data. Are there other potential means of capturing a measure of community response? This paper examines an unexplored resource for noise-related data online, where posts cover a broad range of experiences and opinions. Collecting reader comments in response to online news stories and blog posts, the paper uses content analysis to identify common themes, finding similar and disparate social expectations about noise policy and enforcement among readers from Canada, the United States, the United Kingdom, and Germany.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".