Awareness of Hearing and Sound Education in ear cleaning exercises: a foray into Mathematics education
Bibliographic record
Abstract
The teaching of acoustics has been characterized by a banking model that little contributes to the ideal training of citizens capable of understanding and acting to improve their environment soundscapes. Equally distant from the world of sound and musical culture, audio technology and acoustic environment, it is disconnected from the ever-increasing effort to raise awareness on hearing and sound education, as defended by the Canadian educator Prof. Raymond Murray Schafer. In order to provide elements for reflection on how Mathematics can be itself a language to compete in a sound education, we developed, in a dialogical and problematizing method applied to the technological and cultural world, one further research and teaching with Math students of UNEMAT in Barra do Bugres. This study pointed to the feasibility of educating consciences capable of of improving their acoustic environment, modifying the landscapes where we live, under our responsibility.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".