When <i>answer-phone</i> makes a difference in children's acquisition of English compounds
Bibliographic record
Abstract
Over the course of acquiring deverbal compounds like truck driver, English-speaking children pass through a stage when they produce ungrammatical compounds like drive-truck. These errors have been attributed to canonical phrasal ordering (Clark, Hecht & Mulford, 1986). In this study, we compared British and Canadian children's compound production. Both dialects have the same phrasal ordering but some different lexical items (e.g. answer-phone exists only in British English). If influenced by these lexical differences, British children would produce more ungrammatical Verb-Object (VO) compounds in trying to produce the more complex deverbal (Object-Verb-er) than the Canadian children. 36 British children between the ages of 3;6 and 5;6 and 36 age-matched Canadian children were asked to produce novel compounds (like sun juggler). The British children produced more ungrammatical compounds and fewer grammatical compounds than the Canadian children. We argue that children's errors in deverbal compounds may be due in part to competing lexical structures.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 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".