Do we have what it takes to put all students on the graduation path?
Bibliographic record
Abstract
According to current estimates, more than a quarter of all students and over 40 percent of African American and Hispanic students do not graduate from high school on time. The vast majority of those young people who do not graduate with their peers drop out. The enormous costs to these individuals, their communities, and our society require us to invest in systems that accurately identify young people at risk of dropping out and provide the supports necessary to keep them on track to graduation. This chapter offers a framework for action that calls on communities to identify the scale and scope of the dropout problem and understand why students disengage from school; transform or replace low-performing schools; install early warning and multitiered response systems that provide comprehensive, targeted, and intensive supports to students in and out of school; establish supportive policies and resource allocations; and build community will and capacity so positive changes are deeply implemented and sustained.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".