Bibliographic record
Abstract
The representation of words with multiple pronunciation variants has been widely debated: While the single storage account proposes that all variants of a word are represented by a single, unreduced, representation, the multiple storage account includes lexical representations for different pronunciation variants, including reduced variants. Previous work has provided evidence for the representation of some reduced variants, consistent with the multiple storage model predictions; however, this work has focused on reduced variants that are highly frequent. Using high-frequency reduced variants could pose challenges for the predictions of the multiple storage model since according to some proponents of the single storage model, it can be postulated that while the mental lexicon stores the unreduced variant only, it can exceptionally allow the storage of reduced forms that are considerably more frequent than their unreduced counterparts. To test predictions of the multiple storage model more rigorously, this dissertation examined the storage of low-frequency reduced variants of the uvular stop [ɢ] in Persian. Evidence for the representation of low-frequency reduced variants would only be consistent with the predictions of the multiple storage model, to the exclusion of the single storage model since according to the single storage model, low-frequency reduced variants are not represented in the lexicon. Results from four experiments (production, rating, lexical decision, priming) lent support to the multiple storage model, in that they provided evidence for storage of these variants, despite having low frequency. Taken together, these experiments show that, contrary to the predictions of the single storage model, low-frequency reduced variants can be stored in addition to their unreduced counterparts in the mental lexicon. Additionally, through Persian uvulars as an understudied pattern in an understudied language, this dissertation calls for the investigation of lesser-known languages to assess the broader generalizability scope of models of speech representation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".