Tolerance of Noise in the University Library
Bibliographic record
Abstract
In modern civilization, noise has become a problem in many situations. Traffic noise has been seen to reduce level of health and quality of life (Dratva et. al. 2010). Background noise has been seen to reduce learning ability (Lukits 2012). Even at low levels, noise has been identified as a source of distraction, irritation and low productivity (HR Focus 2006). This study examined the degree to which various sources of noise are a problem to university library users. Preliminary exploratory investigation was conducted with secondary research, as well as informal consultation with library staff, informal discussion with undergraduate classes and first-hand, unstructured observation in the library. With the information gained, a paper questionnaire was constructed consisting of fourteen items representing various sources of noise which were rated on seven-point scales ranging from 1 for least problematic to 7 for most problematic. These were self-completed by 199 library users in various parts of the library over a period of three days at various times of those days. An overall average of the rating was calculated and then the average of the rating from each source was compared with t-tests. Greater tolerance than average was seen for noises that were part of the normal operation of the library. Lower tolerance was seen for sources which were not part of normal operation such as construction, student socializing and cell phones. Interestingly, noises associated with cell phone use, both the ringing of the devices and the talking into them, were less tolerated than average. Also, the level of tolerance of these noises decreased with age of respondent.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".