Bibliographic record
Abstract
Do we design our academic spaces to encourage or discourage student-faculty conversations? Recently, our department moved to a building with new office space. Before the move, our offices were separated from classrooms, and students rarely wandered by because they had to go to another floor and ask permission to talk to us. Our offices were small, half-walled cubicles where no conversation was truly private. We are six months into this new building, but I am still getting used to this new environment where walls actually go to the ceiling, and a closed door actually provides privacy. My office is in the back corner, but students still come down the hall after class. Over the past few months, several students have dropped by my office; I am struck by how much I have enjoyed it. One came asking for advice about an upcoming fieldwork placement, another looking for a reference book for a study group discussion. A student working on a research project came by to share her excitement about a conversation she had just had with a colleague. Then there were the ones who came by after I had handed back some assignments, to actually talk about what they were learning, not just to ask for higher marks. Hope, difficult questions, loss, the pleasure of learning—all of these, and more, have come to visit. It is a joy to connect with students outside of the formality of the classroom and to be able to talk for a few minutes, or pull a book off the shelf, or even just to wave a greeting. So when they come by and say, “Have you got a minute?” I realize that I am relishing this almost illicit pleasure. Should I really taking 5, 15, 30 minutes—30 minutes— just to talk with students because there are some questions on their minds? After all, it is not as if I have nothing to else to do. But, along with the course planning, the classroom teaching, the curriculum evaluation, the committee meetings, the proposal writing, the data analysis, and of course the ubiquitous e-mail, all which promise to remain close at hand as part of the teaching life, nothing is the same as the joy of one to one communication between a teacher and a student. And so often, I am the student. I hope that I never lose the willingness to leave the door open, to listen, to talk for a few minutes. And I sure hope that these students never get too busy to walk down the hall and ask, “Have you got a couple of minutes?” Lynn Cockburn, MEd Ms. Cockburnis lecturer, Department of Occupational Therapy, University of Toronto, Ontario, Canada.
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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.206 | 0.162 |
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".