How to share the process of graduate advising
Bibliographic record
Abstract
This paper starts a two-part series on graduate advising that integrates concepts from adult learning, leadership, and psychology into a conceptual framework for graduate advising. A companion paper provides guidance on how to communicate effectively in graduate advising. Here, we present concepts and tools that enable advisors and graduate students to collaborate effectively and share the responsibility for the student’s learning. We specifically discuss (1) how to promote learning about learning to help students make sense of their experience and identify their supervision needs; (2) how to clarify roles and address conflicts of interest between different roles; and (3) how to establish an effective, learning-centered working relationship. By making the advising process explicit, using the concepts and worksheets presented here, advisors will contribute to the training of the next generation of graduate advisors.
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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 | 0.026 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".