Bibliographic record
Abstract
The purpose of the present study was to investigate how adverse listening conditions affect the ability of normal-hearing listeners to identify the boundaries between discourse topics, or when "what is being talked about" has changed. Twelve subjects (21 to 35 years) listened to digitized recordings of a single speaker's monologues presented in three background noise conditions (+5, 0 and -5 dB S:N). Subjects were asked to push a button when they thought that a change of topic was about to occur in the monologue. Subject responses were analyzed for the latency of topic boundary identification and the number and location of responses. The role of prosodic cues in the identification of topic boundaries was also evaluated. It was found that as the listening condition became less favourable, listeners were slower to identify topic boundaries, were less certain as to where topic boundaries occurred, and relied more heavily on cues to topic initiation than on cues to topic termination for identification of topic boundaries. It was also shown that as the signal-tonoise ratio decreased, listeners were less able to utilize cues to topic boundary that are present in low amplitude utterances such as pitch range and contour, laryngealization and pre-boundary syllable lengthening, but that listeners relied on the prosodic cue of pause duration to identify topic boundaries equally in all three listening conditions.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".