Exploring the Effects of Creating Small High Schools on Daily Attendance: A Statistical Case Study
Bibliographic record
Abstract
Does creating small high schools have a beneficial impact on daily attendance? This question was addressed using time series analysis to examine the case of one urban transfer high school that serves students who previously dropped out of school. This analytical approach is uniquely suitable to examine the dynamical processes characterizing stability and transformation in the system. This school reduced its size from enrolling approximately 900 students up to and through the 2009-2010 school year to about 250 students afterward. We looked at whether attendance was higher after the intervention and whether it was more stable. It turns out that the attendance trajectories over a seven-year period show high volatility prior to the reduction in school size but are more stable afterward. The initial increase in daily attendance at the onset of the intervention is not maintained, but increases are observed later. The study illustrates the relevance of time series analysis for educational policy research as well as the use of complexity theory to fully appreciate the nature of the post intervention changes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".