The CG Equation: A Probabilistic Approach to Predict Doctoral Success Likelihood
Bibliographic record
Abstract
Abstract Doctoral attrition (DA) is a phenomenon of graduate students choosing to discontinue graduate studies and is universally encountered across all academic disciplines. Key parameters, that are typically perceived as valuable by Ph.D. students, are identified from a systematic literature review; and the CG probabilistic equation is formulated to predict the likelihood of a successful Ph.D. experience, called the Doctoral Success Likelihood (DSL), thus minimizing; possibly eliminating DA. Our model provides prospective/novice graduates with a novel framework to self-assess and predict the success likelihood of their Ph.D. journey. Such a framework enables the graduate student to judiciously self-assess and make a rationally informed decision about their career, rather than taking a blind leap of faith. Our equation also accommodates force-majeure circumstances (such as a pandemic, the bereavement of a loved one, mental health issues, etc.), which may significantly impact the time taken to graduate (TTD); leading to a candidate choosing to drop out. Such circumstances typically derail/delay doctoral progress, and can push an initially feasible set of probabilities, into an undesired “infeasibility triangle”. Higher the net probability values obtained from our equation, stronger the likelihood of an enriching Ph.D. experience. When periodically tracked, our proposed equation can also help students identify and calibrate their own doctoral experience, while capturing tangible feedback and perspectives for both students, and supervisors. One author presents his own doctoral journey, applying the CG equation to evaluate DSL values for his Ph.D., over a three-year period.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".