Bibliographic record
Abstract
This paper investigates the way in which people categorize environmental sounds in their everyday lives. Previous research has shown that isolated environmental sounds are categorized on the basis of high-level semantic features when the sounds can be attributed to specific sound sources. However, in the presence of numerous sound sources, as occur in most real-world situations, the process of source identification is often hindered. In the present study, a free categorization task with open-ended verbal descriptions was used to investigate auditory categories for environmental sounds in complex real-world sonic environments. Two main categories emerged from the free-sort, reflecting the absence or presence of human activity in relation to hedonic judgments. At a subordinate level, subcategories were mediated by the participant's reported interactions with the environment through socialized activities. The spontaneous verbal descriptors collected were successful in discriminating categories. These findings indicate that complex environmental sounds are processed and categorized as meaningful events providing relevant information about the environment. The relevance of situational factors in categorization and the notion of auditory category in its relation to linguistic labeling are then discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".