Do You Use Love to Make it Lovely? The Role of Meaning Overlap across Morphological Relatives in the Development of Morphological Representations
Bibliographic record
Abstract
Abstract We examined whether and how the degree of meaning overlap between morphologically related words influences sentence plausibility judgment in children. In two separate studies with kindergarten and second-graders, English-speaking and French-speaking children judged the plausibility of sentences that included two paired target words. Some of these word pairs were morphologically related, across three conditions with differing levels of meaning overlap: low (wait-waiter), moderate (fold-folder) and high (farm-farmer). In another two conditions, word pairs were related only by phonology (rock-rocket) or semantics (car-automobile). Children in both ages and languages demonstrated higher plausibility scores as meaning overlap increased between morphologically related words. Further, kindergarten children rated sentences that included word pairs with phonological overlap as more plausible than second-grade children, while second-grade children rated those with high meaning overlap as more plausible than kindergarten children. We interpret these findings in light of current models of morphological development.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".