Installing Noise Activated Warning Signs in Library Quiet Spaces Does Not Appear to Reduce Actual or Perceived Noise Levels
Bibliographic record
Abstract
A Review of: Lange, J., Miller-Nesbitt, A., & Severson, S. (2016). Reducing noise in the academic library: The effectiveness of installing noise meters. Library Hi Tech, 34(1), 45-63. https://doi.org/10.1108/LHT-04-2015-0034 Abstract Objective – To explore if installing noise activated warning signs (NoiseSigns) in library quiet spaces decreases perceived and actual noise levels. Design – Noise monitoring and user surveys (print and online). Setting – A large university in Canada. Subjects – Users of library quiet spaces where NoiseSigns have, and have not, been installed. Methods – NoiseSigns provide a visual cue informing those present when noise levels exceed a pre-determined level. In this study, researchers installed two NoiseSigns in quiet study spaces previously identified as having the “biggest noise issues” (p. 51), and set the devices to illuminate when noise levels exceeded 65 dB. User surveys investigated respondents’ perceived and desired noise levels via Likert scales before and after NoiseSigns were installed. Actual noise level measurements (via an iPad app) and headcounts were taken manually twice daily for 60 seconds during the same study phases. Additionally, the NoiseSigns recorded noise levels after they were installed. In order to account for variation in library usage over time, control data was also collected in other spaces, where NoiseSigns had not been installed. Main results – A total of 96 surveys were completed and analyzed across all study locations and time periods. One-way ANOVA tests showed there to be no significant difference in perceived noise levels after installing NoiseSigns in any of the intervention areas, in neither the short- or long-term. Respondents’ comments suggested much of the undesired noise originated from social areas adjacent to the quiet study zones or was of a type which would not set off the NoiseSigns (e.g., “people chew[ing] too loud[ly]” (p. 54)). One-way ANOVA tests also found there to be no significant difference in actual noise levels in any of the intervention areas after device installation. Data logging from the NoiseSigns themselves showed the “majority” (p. 56) of noise measurements were in the vicinity of 45-50 dB and “very rarely” (p. 56) did noise levels exceed the 65 dB threshold. Despite this, survey respondents appeared to be unhappy with noise, with mean desired noise levels being lower than those perceived. Conclusion – As a result of the study, the library now strives to have greater delineation between quiet and social spaces. They also seek to ensure doors between these areas are kept closed where possible. Additionally, the authors suggest libraries install noise activated warning signs in social spaces adjacent to quiet study zones in order to keep these spaces from becoming noisy enough to affect nearby quiet zones. Future research could look at the effect of different monitoring options (e.g., security guards, student self-monitoring) and various furniture arrangements on noise levels in the library.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".