Minimizing Doctoral Attrition: Insights to realize the Education 5.0 (E.D. 5.0) vision for STEM fields.
Bibliographic record
Abstract
Doctoral Attrition (DA) is the phenomenon of graduate students discontinuing Ph.D. studies due to individual or combined factors, such as lack of academic integration, insufficient supervisor support, immense publication pressure, gender bias, burnout, and inadequate financial assistance. DA rates vary from 25-40% in Science, 36% in Engineering, and 57% globally. As current pedagogical landscapes evolve to incorporate Education 5.0 (E.D. 5.0) goals, it becomes imperative to support and provide resources to graduate students that foster positive Ph.D. experiences, preparing them to meet Industry 5.0 (I.D. 5.0) competencies post-graduation. Using bibliometric analysis based on our hypotheses-derived Research Questions (RQs) on Scopus, we map out key factors and attributes perceived as most valuable for successful Ph.D. journeys over the past decade, particularly in STEM fields. Recognizing these gaps will help (i) improve student dynamics, (ii) empower personalized learning to meet E.D. 5.0 objectives, and (iii) promote best practices for graduate student success.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".