Bibliographic record
Abstract
This paper examines the identification of stop place and secondary articulation using a free choice task. Russian syllable-initial and syllable-final stops /p pj t tj/ in nonsense utterances were presented to Russian and Japanese listeners (N=30). Correct identification rates for place and secondary articulation of the target consonants were determined based on written responses (in Cyrillic or Katakana). Both groups of listeners showed better identification of syllable-initial stops compared to syllable-final stops. Among the consonants, /p/ was identified better, and /pj/ was identified worse than the other stops. Native listeners performed better than non-native listeners. The overall correct identification rates were lower than (yet strongly correlated with) the rates previously obtained with the same stimuli using a forced choice phoneme identification task. The lower identification rates in the current study can be explained in part by the errors involving the segmentation and syllabification of palatalized stops. Thus, the palatal articulation of the syllable-final palatalized /pj/ was often interpreted as independent of the stop (e.g., /tapj api/ rendered as /taj papi/ or /tjap api/). It is concluded that the free choice task can successfully complement the forced choice task, providing additional information about the perception of secondary palatalization. [Supported by SSHRC.]
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".