Bibliographic record
Abstract
Authors ’ note: The authors wish to gratefully acknowledge the financial support of the Social Sciences and Humanities Research Council of Canada as well as grants from both participating universities to undertake this research. This article provides an analysis of definitions of excellence in graduate study provided by Master’s degree and doctoral candidates, identified by their department as “excellent, ” and by chairs of graduate programs (n = 43) at two western Canadian universities. Faculty members’ definitions tended to focus primarily on external markers of success rather than on personal characteristics of graduate students. Both graduate faculty respondents (n = 20) and graduate student interview participants (n = 23) mentioned the importance of visibility in the department and the community. The graduate student participants made infrequent mention of external indicators, such as grades and ability to garner funding, and attributed their identification as excellent to their own actions and internal attributes. External factors frequently mentioned by graduate students were the cutting edge nature of their research and the importance of the supervisory relationship. Further exploration is needed to develop a working definition of
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| 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 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".