Text features and preschool teachers’ use of print referencing
Bibliographic record
Abstract
Storybook features, such as linguistic richness and print salience, potentially influence how a teacher references print. This study addressed two research questions: (1) to what extent does the linguistic richness and print salience of children's storybooks relate to teachers’ use of print referencing? and (2) to what extent is there an interplay between storybooks’ linguistic richness and print salience when predicting teachers’ use of print referencing? Fifty‐seven teachers participated in a whole‐group read‐aloud intervention while including print‐referencing behaviours. Researchers analysed teacher videos in order to (1) document teachers’ use of print referencing and (2) document the storybook's linguistic features and print salience. Results indicated that print salience is highly positively related to teachers’ use of print referencing. Also, the constructs of Print Salience Metric (PSM) and total words predicted teachers’ use of print referencing, and the interaction between PSM and mean length of sentence was significant for storybooks with moderate and high PSM scores.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".