Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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 it