Evaluating the psychometrics of the SSIS SEL in a socio‐economically diverse sample
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
Abstract The increasing interest in implementing social‐emotional learning (SEL) interventions within schools calls for more reliable and valid assessment tools to measure the effectiveness of SEL programs. We investigated the factor structure and psychometric properties of the Social Skills Improvement System – Social‐Emotional Learning Edition (SSIS SEL) with a group of ethnically and socio‐economically diverse grade 3 students in Ontario, Canada ( n = 427). The SSIS SEL is an age‐normed measure used for the evaluation of interventions and prevention initiatives. Data collected included both teacher‐report and student self‐report measures. The factor structure proposed by the scale developers was not replicated using this smaller single grade sample. However, the study demonstrates evidence of good construct and convergent validity supporting the novel factor structure. Results from this study suggest that more research on the SSIS SEL's application in real‐world intervention research programs is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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