Considerations Regarding Graduate Student Persistence
Bibliographic record
Abstract
Completion of graduate studies is a central issue for universities. Over the past decade researchers interested in higher education have become concerned with graduate student completion rates. Possible reasons underlying variations in graduate student persistence have included the amassed learning experiences and subsequent perceptions of graduate students, supervisory committee members, and other department staff. This article addresses some of the psychosocial considerations that underlie the complex interactions among students, supervisory committees, and departmental support staff, referred to here as the "academic triad." Using Seligman's (1991) explanatory framework and Bandura's (1986) self-efficacy theory, this article explains how student persistence is closely tied to the behavior of students, academics, and departmental support staff. Further, the article provides two frameworks to gain a broadened understanding of the relationship between the academic triad and graduate student persistence. Recommendations are provided as to how to foster graduate student persistence through improved personal and interpersonal reflexivity.
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How this classification was reachedexpand
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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 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 teacher head, 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".