Factors in Word-Final /t/ Reduction and Deletion in German
Bibliographic record
Abstract
Sound reduction and deletion have been studied across many languages for some time. Usage-based approaches suggest that the more often a word is used, the more likely it is that some of the sounds are reduced. Phonetic environment, stress, and speech rate have all been studied as reasons for sound reduction or deletion. Most recently, frequency in reducing context (FRC) has been included when studying sound reduction and deletion. FRC in this thesis measures the portion of word tokens of a given word type that are followed by a reducing context. This thesis focuses on word-final /t/ reduction and deletion in German. Audio and transcriptions of six native German speakers were time aligned with the Montreal Forced Aligner for the analysis. Word frequency, phonetic environment, stress, and FRC were analyzed as factors that condition reduction and deletion. A linear mixed-effects regression model with the dependent variable of word-final /t/ duration found significance between a shorter /t/ duration and (1) a shorter duration of the preceding sound and (2) a consonant preceding the reduced /t/. A logistic mixed-effects regression model with the dependent variable of word-final /t/ deletion found significance between deletion and (1) a consonant preceding the deleted /t/ and (2) word frequency. Though FRC was not found to be significant in this study, perhaps measuring FRC with a different reducing context would be significant in a future study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".