Eyes on the prize: Patterns of risk and resiliency in high school dropout
Bibliographic record
Abstract
Although there has been extensive research on the independent predictor variables of high school drop out, less research has been dedicated to explaining the relationships among these variables. This exploratory study examined the relationship between socioeconomic status (SES) and academic self-efficacy, specifically to see if delay discounting could be acting as a moderator between the two variables. Participants were 20 high school students from a medium-sized city in Western Canada, all enrolled in a dropout prevention program. Data was collected via surveys on three separate occasions throughout the program. The results indicated a non-significant positive correlation between SES measures and academic self-efficacy. Delay discounting, defined as lack of willingness to wait for larger, but delayed rewards, had a non-significant negative correlation with both academic self-efficacy and two of three SES measures. Delay discounting was a significant moderator of the relationship between SES and academic self-efficacy. Lastly, the early school-leaving sample was found to have significantly higher levels of delay discounting than a college-based comparison sample. These findings suggest that the individual difference variable of delay discounting may help explain inconsistent relationships between socioeconomic background and likelihood of academic success.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".