Bibliographic record
Abstract
Whether we are nudging the world toward cleaner water, widespread food security, enhanced intercultural understanding, or any other envisioned future, the work of "building a better world" (Hartman, Kiely, friedrichs, & Boettcher, in press) that is at the heart of democratic civic engagement (DCE) is a matter of questioning and learning and acting.We believe the Scholarship of Teaching and Learning (SoTL) -i.e., inquiry into learning -has the potential to further deepen our ability to question, learn, and act together -especially when it is understood and enacted through the values and practices of DCE.By that, we mean when it (a) positions all involved as co-teachers, co-learners, and co-generators of knowledge and practice, and (b) takes as a goal the development of civic capacities in those doing the inquiry.This challenges traditional roles and relationships in teaching and learning and the way we study them that too often frame students, community members, and staff merely as objects of study by expert faculty.We believe that SoTL can and should be enacted democratically, with everyone involved co-creating the questions and the processes that help us learn about learning; and we invite everyone involved in servicelearning, civic engagement, and SoTL to move in this direction.What might such SoTL look like?Patti describes a glimpse:
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
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".