Reducing noise in the academic library: the effectiveness of installing noise meters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the effect of an electronic noise-monitoring device (NoiseSign) at reducing noise levels in quiet study areas in an academic library. Design/methodology/approach – Surveys and decibel-level measurements were used to measure the perceived and objective noise levels, respectively, in both an intervention and a control area of two major branch libraries. Patrons’ perception of noise was measured with a passive paper and online survey, which asked patrons to rate the current noise level and their desired noise level. The actual noise measurements were collected twice a day with a hand-held decibel reader for 60 seconds and then corroborated after the intervention with automatically logged decibel readings from the noise monitor device in the two intervention areas. The authors conducted one-way ANOVA’s to determine if the results were significant. Findings – The NoiseSign had no statistically significant effect on either actual noise levels or user perceptions of noise in the library. The surveys comments and anecdotal observation of the spaces while doing measurements did reveal that noise in the quiet study areas was not the primary source of complaints. Originality/value – In spite of many proposed solutions to reducing noise in libraries, there has been very little research in this area. This is the first study to examine the effectiveness of using a noise-monitoring device in reducing noise levels at an academic library.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it