Revisiting the Tinto's Theoretical Dropout Model
Why this work is in the frame
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Bibliographic record
Abstract
In the context of university higher education at undergraduate level, the model of student-institution integration, proposed by Tinto & Cullen and later refined in some of its parts, has often been used to explain the process of dropout/persevere, and even to anticipate such events. This paper approaches the evolution of the Tinto's model since its proposal and reports an analysis of versions of the model found in the literature. The conducted analysis was directed with focus on how to make the model operational, in a way that it could be implemented by universities as an academic computational support system for predicting dropouts. Aiming at its future computational implementation, the analysis approaches the model in relation to its lack of precise definitions of some concepts employed, the loose specification of both, variables and processes involved and questions the extreme importance given to the social integration aspect experienced by students, for explaining dropout.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it