Fickle fricatives: Fricative and stop perception in Gurindji Kriol, Roper Kriol, and Standard Australian English
Bibliographic record
Abstract
This paper uses a 2AFC identification task experiment to test listener perception of voiceless fricative-stop contrasts with minimal pairs modified along a 10-step continuum. Here, the authors focus on the uniqueness and near-uniformity of the phonological systems found in Australia. The languages involved in this study include Roper Kriol (an English-lexifier creole language), Gurindji Kriol (a mixed language derived from Gurindji and Kriol), with Standard Australian English (Indo-European) used as a baseline. Results reveal that just over 50% of the Roper Kriol and Gurindji Kriol listeners identified differences in the stop-fricative pairs with a high degree of consistency while nearly a quarter consistently identified the fricative-like stimuli as such, but showed random responses to the stop-like stimuli. The remaining participants showed a preference toward the fricatives across the entire continuum. The authors conclude that the fricative-stop contrast is not critical to the functionality of the phonologies in Roper Kriol or Gurindji Kriol, which could explain the high degree of variability. In addition, there is some evidence that the degree of exposure to English may have an effect on the degree of contrastability.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".