How Children Become Sensitive to the Morphological Structure of the Words That They Read
Bibliographic record
Abstract
Background. We tested the predictions of models of word reading development as to the effects of repeated exposure on reading of derived words. Aim. Our goal was to examine the impacts of variables that quantify different aspects of this exposure: base frequency, family frequency, and family size. Methods and Samples. In Experiment 1, we asked 75 children in Grades 3 and 5 to read derived words with low surface frequencies (e.g., questionable) that varied in base frequency, family frequency, and family size. In Experiment 2, we asked 41 adults to read the same set of words. Results. In Experiment 1, only base frequency made a contribution that was independent of the other two variables of interest (family size and family frequency) and the control variables (surface frequency, semantic relatedness, and neighbourhood size). In Experiment 2, a similar pattern of results emerged, this time on reading speed. Conclusions. Together, results of these two studies suggest that base frequency has a special role in both children’s and adults’ reading of morphologically complex words. These findings suggest that it plays a specific role in development and maintenance of sensitivity to morphological structure in reading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".