The production and perception of Ikema geminates
Bibliographic record
Abstract
Gemination is largely assumed to be cued through duration. While production and perception studies consistently confirm the importance of duration as a cue, certain phones are not as easily distinguished using duration alone. The current study analyzes geminate and singleton minimal pair production and perception in Ikema, an endangered Japonic language, to determine to what extent other cues may contribute to this distinction. Productions of stop, fricative, affricate, nasal, and rhotic geminates and singletons were used to determine acoustic differences and possible cues to gemination. We analyzed duration for all phones, and spectral moment and vocalic measures for fricatives and affricates. Results indicate that while duration is an important cue, spectral moment measures and vocalic measures are better predictors of affricate geminates and contribute significantly to the acoustic distinction among fricatives. Word-initial stops may be difficult to identify as closure duration is a more significant predictor of gemination than voice onset time. Listener perception was analyzed using a lexical decision task. Results indicate that while Ikema speakers distinguish geminates from singletons in novel situations, non-word perception may not be consistent cross-linguistically due to varying exposure to the concept, and community-internal acceptability of pronunciation variation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".