Bibliographic record
Abstract
This dissertation presents a narrative research carried out with students of Music from Universidade Federal de Santa Maria (UFSM), it studies the possible relations between Acoustic Ecology and the training of musical educators,. Acoustic Ecology studies the relationship between beings and their society mediated through sound, the effects of the soundscape. The works of the Canadian composer and musical educator Murray Schafer brought attention to the role of sound, questioning what musical education could do about any of the world´s environmental problems and the quality of the global soundscape. Schafer on A Sound Education brings many exercises to practice active listening. Among those exercises we find Sound Diaries, which brings acoustic ecology close to narrative research, in this case with the narrative of a soundscape. On this research we proposed 15 meetings containing expositions and discussions about acoustic ecology, The participants wrote sound diaries and composed sound art, that being part of the meetings and the research. We identified that the knowledge acquired as in the graduation course, and also, past life experiences come together with the new knowledges of Acoustic Ecology, creating new meanings for everyday life, and creating a new environmental conscience and a listening conscience, having an effect on their path as a music teacher.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".