Bibliographic record
Abstract
Welcome to Volume 5, Issue 1, of Imagining SoTL, which connects with both the 2023 and 2024 Symposia. In this issue, we share an episode of SoTL in 60, a new initiative at the Fall 2024 Symposium. In these spontaneous and brief podcast episodes, Sally Haney interviews participants at the conference, who provide a rich window into the experience. In the episode shared in this issue, the keynote speakers—Sarah Bunnell and Josh Hill—discuss how their opening and closing addresses intersect and connect. The first article is Leanne Vig’s reflective essay on Julie Rattray’s keynote from the previous year (2023). In the second article, Leda Stawnychko provides an account of a SoTL study on the insights gained from analyzing reflections of students engaged in an undergraduate leadership course.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.215 | 0.137 |
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".