Teach first, research questions later: Understanding the role of the college teacher-scholar for “The spectrum of the professoriate and the rise of the teaching stream”
Bibliographic record
Abstract
Conference paper presented at the <a href="https://accute.ca/accute-conference/">ACCUTE (Association of Canadian College and University Teachers of English)</a>conference, Calgary 2016.\n\nLast year at Congress, I presented a paper titled “Off the Sides of Our Desks: Research in a Community College Context” on a Professional Issues panel here at ACCUTE. I’m heartened to be asked back to this panel to discuss these issues further as part of a larger conversation about teaching-centred work in the academy, and I’m grateful to the organizers for the opportunity. I think we are at a crossroads in the profession wherein we can either find a way to support teaching-focused academics in remaining part of the scholarly conversation, or risk losing recent PhD graduates from our community. This short paper is a bit of a meditation on why that is, and what comes next.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".