A sound education: Becoming aware of the sound around us
Bibliographic record
Abstract
In this article it is intended to discuss the issue of noise pollution from an unusual point of view: noise pollution is not only the result of sound increase worldwide, but, particularly, the poor quality of our listening habits in modern life as well. In contemporary society we are subject to a considerable amount of stimulus to all our senses: vision, scent, taste and hearing which are becoming more and more insensible due to over exposure in our environment. These increased stimuli make us look for alternatives to reduce our ability to perceive them and be protected from injuries. However, our sensitivity will also decrease. In the specific case of environment noise, over exposure has made us forget the enchantment of certain sounds that used to give us pleasure or evoke good feelings by many ways, making us recall certain good things, bringing particular moments of our lives to our memory or even filling us with strong emotion. The Canadian composer and music educator, R. Murray Schafer, believes that noise pollution is the result of a society who became deaf. Closing our ears to noise protect us from noise pollution but also prevent us from grasping subtleties of listening. Contemporary world does not help us to be aware of sound in the space around us; acquiring this hearing ability is a matter of focus, interest and practice. Sound education exercises are aimed at children, teenagers and adults who want to improve their listening ability to environmental sounds, perceive its proprieties and learn how sound affects us and touches our feelings. The results are easy to accomplish and contribute to our awareness of the sound environment around us and to the conception of the environmental sound as a composition made by everybody and everything through positive actions, strong will and high sensitivity. Copyright © (2011) by the International Institute of Acoustics & Vibration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".