Bibliographic record
Abstract
I am honored to have been asked to contribute to the APS Mentoring Forum, especially at such an early point of my own career. In terms of my own training journey, I trained at the University of Calgary, Faculty of Medicine, which is a research-focused institution. Here, faculty face high exceptions to land external grants to fund their research and focus on research with advanced trainees (masters, doctoral, and postdoctoral), and are evaluated almost exclusively on research productivity. The teaching they do amounts to a few lectures or course coordination in graduate level classes in their area of expertise. It was an incredible place to train in neurobiology. However, most trainees, when they get out, do not end up with jobs at institutions like this. Following completion of my PhD, I landed a tenure-track instructor position at an undergraduate teaching college, where students completed the first 2 years of their programs and transferred to bigger institutions. At the time, I had heard there were efforts to transform the institution into a bachelor degree granting institution, and I wanted to be a part of the process. We completed this transition in 2010, in both name and membership in the Association of Universities and Colleges of Canada. We rolled out many new degree programs, and I was and continue to be involved in both new curriculum and course development. The transition of Mount Royal College to Mount Royal University in 2010 has come with a lot of changes and growing pains, not the least of which were around academic rank and work pattern transitions to include scholarship. My focus in this article will be around workload and scholarship support in a new university setting that focuses its efforts on teaching and training undergraduates. My comments are aimed at relatively new faculty who are trying to start a research program with the inclusion of undergraduate students at a teaching-focused undergraduate institution. Additionally, instructors at small liberal arts colleges may find my experience helpful if they want to get started, either at their own institutions or in collaboration with other universities. If you are a Chair or a Dean interested in supporting the development of research programs, my experiences outlined below may help in framing the justification for and in securing resources to support these activities. Post-PhD, not everyone is going to get jobs in researchintensive institutions. If you are one of those new to mid-career faculty members at a small teaching-focused undergraduate institution trying to get started with a research program, this column is aimed at you.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.026 |
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".