Bibliographic record
Abstract
When the acoustics are poor, a listener's ability to navigate an auditory scene, communicate within it, or learn while immersed in it, is adversely affected (see, for example, Picard & Bradley's, 2001 analysis o f classroom acoustics).Moreover, when poor acoustics are combined with virtually any kind o f auditory problem (even those which would not normally merit clinical attention), all of these listening and learning difficulties are considerably exacerbated.For example, a number o f studies have demonstrated that older adults with clinically normal hearing are considerably more disadvantaged than normal-hearing younger adults in adverse listening conditions (e.g., Schneider, Daneman, Murphy, & Kwong See, 2000).Indeed, hearing status in older adults is, arguably, the best predictor o f their performance on a number o f different cognitive tasks.For example, in the 1994 Berlin Aging study (Lindenberger & Baltes, 1994), the hierarchical model that provided the best account o f age-related declines in cognitive functioning was one in which age effects on cognitive tasks were mediated, in large part, by age-related changes in auditory function.Because the proper functioning o f higher-order cognitive processes can be highly dependent on the integrity o f the information supplied by the sensory systems, it is not unreasonable to expect that cognitive functions dependent on sensory input might be adversely affected by poor acoustics.Hence, acousticians, audiologists, psychologists, and cognitive scientists need to understand how acoustics and cognitive functioning are related.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".