Phonological redeployment for [retracted tongue root] in third language perception of Kaqchikel stops
Bibliographic record
Abstract
Phonological redeployment is the theoretical ability of language learners to utilize non-local phonological knowledge from known languages in the mapping and acquisition of novel contrasts in their target languages. The current paper probes the limits of phonological redeployment in a third language acquisition scenario. The phonological features [Advanced Tongue Root] and [Retracted Tongue Root] capture a range of phonological contrasts and harmony processes in both vowels and consonants of spoken languages across the world, including, but not limited to, vowel tensing and post-velar places of articulation (e.g. uvular). Kaqchikel (cak) exhibits both a tense-lax vocalic contrast in its vowels plus a velar-uvular Place contrast in its eight stop consonant phonemes. English (eng) exhibits a tense-lax vocalic distinction but no velar-uvular distinction among its six stop phonemes. Spanish (spa) exhibits neither of these contrasts in its vowels or among its six stop phonemes. How do multilingual learners of Kaqchikel already familiar with English and Spanish, but who differ in which is their first language (L1), compare in their categorical perception of Kaqchikel stop consonants? Despite English and Spanish having a three-way Place distinction among stops in common, in a phonemic categorization task, L1 English learners of Kaqchikel were better at correctly categorizing audio recordings of Kaqchikel uvular stops than L1 Spanish learners of Kaqchikel. To account for this surprising result, I propose that the L1 English group have easier access than the L1 Spanish group to the feature underlying English's tense-lax distinction. This access allows them to redeploy that phonological feature to accurately map out the novel four-way contrast of Kaqchikel's stop consonants, and the [±RTR] specified velar-uvular distinction in particular. Therefore, phonological redeployment must be considered in models of third language acquisition.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".