Bibliographic record
Abstract
The construct of intelligibility in L2 speech has primarily been operationalized functionally in terms of speech being classified as intelligible if the listeners successfully recovered the intended message (Munro & Derwing, 1995). In this paper, I will operationalize intelligibility psycholinguistically in terms of spoken word recognition. We do not need to invoke any special machinery for intelligibility in bilinguals; monolinguals and bilinguals process speech in the same way (Libben, 2000; Libben & Goral, 2015). Listeners have to segment the speech stream and the parser maps the phonetic elements onto higher-level linguistic representations such as phonemes, syllable nodes and metrical feet. The role of experience in the listener is modelled analogously to high-variability phonetic training (HVPT) via broadening the prior likelihood (in a Bayesian sense) of the mapping of an L2 phone onto an extant phonological category. I conclude by discussing pedagogic implications, and suggesting that pedagogic models that advocate a single non-native variety of English, which will be intelligible to all ears (i.e. parsable by all grammars), are problematic psycholinguistically.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".