Using behavioral and academic indicators in the classroom to screen for at‐risk status
Bibliographic record
Abstract
Abstract The present study validated a brief at‐risk screening instrument designed for easy use by teachers in the elementary school. School performance measures were collected for students in first to sixth grade one year following initial teacher ratings using the Screening For At‐Risk Status screening instrument. Findings indicated that the instrument is best seen as measuring a single at‐risk construct with items drawn from three domains: academic skills, social confidence, and social cooperation. Correlations between at‐risk scores and school performance measures taken one year later demonstrated predictive validity. The screening instrument correctly identified at‐risk students with 88% accuracy and not‐at‐risk students with 74% accuracy. There were 12% false negatives. Use of the instrument provides teachers with a quick, easy screening of students who may develop difficulties in the future. For schools, the screening can be used as the first step in a supportive response system to assist at‐risk students from developing serious school difficulties and possibly failure in the longer term. © 2005 Wiley Periodicals, Inc. Psychol Schs 42: 151–158, 2005.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".