Language generality in phonological encoding: Moving beyond Indo‐European languages
Bibliographic record
Abstract
Abstract Theories of phonological encoding are centred on the selection and activation of phonological segments, and how these segments are organised in word and syllable structures in online processes of speech planning. The focus on segments, however, is due to an over‐weighting of evidence from Indo‐European languages, because languages outside this family exhibit strikingly different behaviour and require the processing of additional phonological structures. We review evidence from speech error patterns, priming and form encoding studies, and re‐syllabification in several non‐Indo‐European languages, including Mandarin, Cantonese, Japanese, Arabic, Hindi, Korean, Thai, and Vietnamese. We argue that these languages deepen our understanding of the nature of phonological encoding because they require recognising language‐particular differences in: the first selectable (proximate) units of phonological encoding, the phonological units processed as word beginnings, the dynamics of syllable emergence during encoding, and the varied manifestations of re‐syllabification. A satisfactory and general account of phonological encoding must incorporate these rich phenomena.
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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 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".