When Predictions Fail: The Case of Unexpected Pathways Toward High School Dropout
Bibliographic record
Abstract
This study examines childhood variables that tend to deflect life‐course trajectories away from finishing high school. We examined unexpectedly graduating in the presence of three empirical risk factors (having a mother that did not finish high school, being from a single‐parent family in early childhood, and having repeated a grade in primary school) and unexpectedly not graduating in the absence these same factors (low risk). The comparison groups comprised individuals who expectedly did not graduate (first case) and expectedly graduated (second case). We found that having experienced all three factors practically guaranteed not finishing high school, thus defining a crystal clear target group for policy. Without screening, intervention, and follow‐up, individuals facing such cumulative risk are most unlikely to graduate. We also found a group of males and females that did not finish high school despite not having these three risk factors. These missed estimates become nontrivial once they are translated into a population‐level statistic of lost human capital investments. Specific family and individual factors helped explain the unexpected life course toward not finishing high school, especially for low‐risk males and females. Our results suggest policies that support childhood screening for attention‐related difficulties and helping parents better understand supervision during adolescence.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".