Effects of the variation of phoneme duration on word processing
Bibliographic record
Abstract
Contextually predictable, high frequency, competitor-dense words are often produced with less contrastive categories in informal conversation (Plug, 2011; Gahl et al., 2012; Tucker & Ernestus, 2016). One of the more frequent ways this manifests is through changes in phoneme duration, with shorter duration related to less careful speech (Gahl et al., 2012). However, initial observations point to large temporal variation occurring even in isolated words produced in controlled settings. The present study investigates how temporal variation affects processing speed for single words. A number of measures of temporal variation (e.g., word mean standardized phoneme duration) are compared, while controlling for a variety of psycholinguistic variables. Data from the Massive Auditory Lexical Decision project (Tucker & Brenner, 2016) was used. 232 native speakers of English responded to a subset of 26800 words from a full range of word types produced in isolation by a single speaker. Temporal variation measures are assessed based on their contribution to models predicting participant response latencies. Results offer insights into different operationalizations of temporal variation at the word level and how this durational variation influences speed of processing.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".