Language sample analysis: development of a valid language assessment tool and determining the reliability of outcome measures for Farsi-speaking children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study determined how to elicit language samples from Farsi-speaking children, which language measures should be analysed, and whether these analyses are reliable. Two valid sets of picture stories were developed to elicit the language samples. Language measures were chosen by a panel of experts and the reliability of the measures was verified by test–retest reliability. The subjects were children 5–6 years of age (N = 30) who told stories twice at a 7–10 day interval. The results of inter-rater reliability showed that consistency of measurement was high for the transcription and analysis of the stories. The results of test–retest reliability showed there was a correlation between most variables in the longer samples (p < .05). This study demonstrates that language ability can be more reliably assessed when longer language samples are collected.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it