The Impact of Nudge Letters on Improving Attendance in an Urban District
Bibliographic record
Abstract
This study evaluates a “nudge letter” to parents intervention designed to reduce chronic absenteeism among students in one urban district. Using a regression discontinuity design (RDD), it estimates the impact of the intervention on improving student attendance. The forcing variable for the RDD was 2016–2017 attendance rate, with a “threshold” of a 0.90 attendance rate (missing 10% of days). Analyses established demographic equivalence of students in the 0.88 to 0.92 baseline attendance bandwidth. Although the overall impact of the intervention on attendance change between Fall 2016 and Fall 2017 (first-quarter attendance) was small and non-significant (ES 0.09, p = .20), the effect size for middle school students (0.34, p = .044) was “substantively important” by What Works Clearinghouse standards. The effect of the intervention on the full year’s attendance rate was not significant.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".