The Contributions of Activity and Occupation to Young Children's Comprehension of Picture Books
Bibliographic record
Abstract
This article provides an empirical examination of current conceptions of activity and occupation by analyzing factors affecting children's comprehension of a picture book. Childhood activities are depicted prominently in picture books, and given that children are believed to rely extensively on personal experience when interpreting stories, it is reasonable to expect children's experience of engaging in these activities to play an important role in story comprehension. Twenty seven young children, on an individual basis, followed along as a picture book about a child taking a bath was read. The children then participated in retelling the story by supplying the next word or two when the story‐teller paused in the retelling. Level of comprehension, as indicated by the number of story elements correctly supplied, was analyzed in relation to working memory capacity and vocabulary level as measured by the Woodcock‐Johnson Psycho‐Educational Battery and the Peabody Picture Vocabulary Test, respectively. A substantial partial correlation was found between story comprehension and working memory when controlling for vocabulary level, suggesting that children's understanding of the story involved much more than processing the meaning of activity words. Consistent with Pierce's (2001) conception of activity and occupation, this result suggests that occupations are not merely activities; they are activities suffused with personal meaning by contexualization in an individual life.
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 | 0.013 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".