“Undating” and “Quarter Ponies”: Comparing the Use of Derived Words and Compounds Across Discourse Types and Age Groups
Bibliographic record
Abstract
Purpose Derivational morphology and compounds are important aspects of academic vocabulary. However, investigation of the development of expressive derivational and compound morphology using language sampling is sparse. This cross-sectional study used three types of language samples to investigate quantitative and qualitative changes in the spontaneous production of derived words and compounds in early and late elementary–age children as a function of age and discourse type. Method Twenty-three children in two age groups (early elementary, n = 12; late elementary, n = 11) participated. Three types of language samples were elicited: conversational (10-min conversation with an adult examiner), narrative (“I tell–you tell” narrative with single picture stimulus combined with a story stem narrative), and expository (explanation of how to play a favorite game or sport with text-based topic prompts). Language samples were transcribed using Systematic Analysis of Language Transcripts (Miller & Chapman, 2012) conventions with the addition of researcher-created codes to identify derived words and compounds. Quantitative measures (number of derived words, different derived words, number of compounds, and different compounds) were calculated as percentages of total words or number of different words to control for differing sample length. The types of derivational morphemes and compounds produced by children in each age group were listed and qualitatively analyzed for evidence of a sequential development of specific morpheme types, variation in complexity, and productivity. Results Developmental change in quantitative and qualitative measures of derivational and compound morphology was evident across early and late elementary–age children in the language samples. Lists of derived words and compounds produced provided a rich source for analyzing developmental patterns in expressive morphology. Conversational and, to a lesser extent, expository discourse generated the greatest number and diversity of multimorphemic words. Conclusions This research provided new insights into academic vocabulary development in elementary school–age children. The clinical usefulness of language sampling to quantitatively and qualitatively assess derivational morphology and compounds was demonstrated. Supplemental Material https://doi.org/10.23641/asha.12170373
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".