Re-Evaluating the University Attrition Statistic
Bibliographic record
Abstract
Following up on the transition to university and university persistence, 119 (44 males; 75 females) students who had not graduated (within seven years) from a large, commuter Canadian university were interviewed. ‘Leavers’ were nota homogenous group but could be divided into categories of departure: transferred to another university (29.4%), transferred to college (29.4%), took temporary leave (11.8%), dropped out (20.2%), and put on academic probation (9.2%). Quantitative analyses indicated that transfer and noncontinuing students differed on previously collected measures of achievement and parental reciprocity (Wintre & Yaffe, 2000). Interview data demonstrated that reasons for leaving were more related to mobility, exploration and career paths, characteristics of emerging adulthood, than to negative university experiences. Furthermore, many former students completed their degrees elsewhere, decreasing the previously reported attrition rate from 42.1% to 22.5%.
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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 | 0.116 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".