Wordform variability in infants' language environment and its effects on early word learning
Bibliographic record
Abstract
Traditional views of language development suggest that noun learning involves creating a one-to-one mapping between concrete objects and their labels. In the current work, we provide evidence that real world language input to infants does not provide such tidy mappings. Instead, infants encounter many variant wordforms for familiar nouns(e.g. dog∼doggy∼dogs). We explore this wordform variability in 44 English-learning infants’ naturalistic environments using a longitudinal corpus of infant-available speech. We looked at both the frequency and composition of wordform variability. We found two broad categories of variability: morpheme-adding changes, where words were pluralized or compounded (e.g. coat∼raincoats); and wordplay, where words changed form without any associated change in meaning (e.g. bird∼birdie). Wordplay occured with a limited number of lemmas that were usually early-learned, highly-frequent, and shorter. When looking at all wordform variability, we found that individual words with higher levels of wordform variability were learned earlier than words with fewer wordforms, over and above the effect of frequency.
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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 | 0.006 |
| 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 | 0.000 | 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".