Assessing the Reliability and Use of the Expository Scoring Scheme as a Measure of Developmental Change in Monolingual English and Bilingual French/English Children
Bibliographic record
Abstract
Purpose: The Expository Scoring Scheme (ESS) is designed to analyze the macrostructure of descriptions of a favorite game or sport. This pilot study examined inter- and intrarater reliability of the ESS and use of the scale to capture developmental change in elementary school children. Method: Twenty-four children in 2 language groups (monolingual English and bilingual French/English) and 2 age groups (7-8 years, 11-12 years) participated (6 in each subgroup). Participants orally explained how to play their favorite game or sport in English. Expository discourse samples were rated for 10 macrostructure components using the ESS. Ratings were summed for a total score. Results: Inter- and intrarater reliability was high for the total ESS score and for some but not all ESS components. In addition, the total score and ratings for many ESS components increased with age. Few differences were found in use of macrostructure components across language groups. Conclusions: The ESS captures developmental change in the use of expository macrostructure in spoken discourse samples. It may be beneficial to take into account the lower reliability found for ratings of some ESS components in clinical practice. Due to the small sample size, these results should be considered preliminary and interpreted with caution.
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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 | 0.006 | 0.017 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".