Overgeneralization in the processing of complex forms in Valley Zapotec child language
Bibliographic record
Abstract
Models of language learning and processing differ in their level of emphasis on the storage of individual meaningful units versus combinations of meaningful units. While there is evidence for the storage of larger stretches of speech, a separate issue is how much such stored forms contribute to processing, as compared to morphologically simpler forms. We examine the acquisition of one aspect of the phonology of Valley Zapotec: complementarity of segmental length based on subsegmental features: vowels before fortis consonants are short (VCː), and vowels before lenis consonants are long (VːC). This complementarity is found for fortis consonants in morphologically simple forms with final stress (simple nouns, verbs with full subject noun), but not in morphologically complex forms with a final unstressed syllable (diminutive nouns, verbs with pronominal subject clitic). During one period of development, Zapotec-learning children overgeneralize the complementarity from morphologically simple to morphologically complex forms (with u-shaped learning likely). The child’s processing of complex forms in language production is based more on simple forms than on the complex forms themselves. We identify five possible explanations of these results. Insofar as combinations of morphemes are stored at this young age, they are relatively ineffective at influencing processing during language production.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".