Enhancing Learning Experiences of Graduate Students in the Faculty of Engineering and Applied Sciences at Memorial University of Newfoundland
Bibliographic record
Abstract
The Faculty of Engineering and Applied Science (FEAS) at Memorial University of Newfoundland (MUN) offer 17 unique programs to over 500 graduate students. In addition to providing financial support, office space, courses, and supervision to students, FEAS has developed an interconnected series of programs, seminars, and workshops to help graduate students succeed in their studies, research, and life after graduation. Among these are the Graduate Seminar Course, the TA Training Program, the Outstanding TA Award, regular professional development seminars, the Graduate Mentorship Program, in addition to numerous EDGE (Enhanced Development of the Graduate Experience) programs and workshops offered by the School of Graduate Studies. This suite of academic and professional supports plays a critical role in FEAS’s goals and represent innovative and significant work that foster graduate student success. This paper describes these innovative strategies and demonstrates FEAS’s and MUN’s commitment to providing outstanding opportunities for students to grow and succeed in their graduate studies
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".