Investigating sonic injustice: A review of published research
Bibliographic record
Abstract
Sound has been researched as either an environmental pollutant (noise) with detrimental health effects or an environmental resource with beneficial outcomes for well-being. We define sonic injustice as unjust inequalities in both noise exposure and access to high-quality, beneficial sound environments. We performed a comparative analysis of 34 peer-reviewed studies on sonic injustice. These studies were from Europe, North America, Accra and Hong Kong. We found suggestive evidence of a social inequality in noise exposure, particularly for low income and racial/ethnic groups. In contrast, children were often associated with an underexposure to noise. We did not find any studies on inequalities in access to beneficial sound environments, except for one study on quiet areas. As well, this review identifies trends in European and North American studies; discusses causal mechanisms for sonic inequalities; and presents avenues for future investigations into sonic injustice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".