Building and enjoying the “big tent” together: A review of ISSOTL17
Bibliographic record
Abstract
The International Society for the Scholarship of Teaching and Learning 2017 conference (ISSOTL17), cohosted by the University of Calgary and Mount Royal University, invited attendees to ponder aspirations, anxieties, adventures, and new horizons under the theme of Reaching New Heights.Hosting over 600 delegates in Calgary, Canada, for ISSOTL17 was no small feat, and here I share a glimpse into how student partnerships manifested in planning and holding the society's flagship event.In the Scholarship of Teaching and Learning (SoTL), the "big tent" metaphor (Huber & Hutchings, 2005) describes the individuals from many disciplines and perspectives who come together to converse about teaching and learning.Engaging students as partners in SoTL is considered good practice (Felten, 2013).Inviting students to build the "big tent" alongside faculty (and staff) showcases how the society values student collaborations.Poole and Chick (2016, p. 3) argued as "we are all still trainees in our own ways", students and faculty both contribute ever-developing knowledge and skills to a collective expertise.In helping host the conference, I learned a lot from faculty, but also they often asked for my expertise-we learned together.Broadly, partnership relies on "respect, reciprocity, and shared responsibility" (Cook-Sather, Bovill, & Felten, 2014, p. 1).Although conference planning may not impact teaching and learning practice directly, the ISSOTL conference sets the tone for how student partnerships are discussed, enacted, and perceived in the wider SoTL field.In late 2016, I joined the ISSOTL17 program committee as a member of the society and as a student of the University of Calgary.In partnership with the conference organizers, students led and supported critical elements such as the conference commons, the program, the video-trailer, volunteers, submission reviews, and newcomer initiatives.Throughout, I never felt my contributions were perceived as lesser-than compared to faculty (or staff).Expectations and trust were high for everyone, and when issues arose, students and faculty tackled the situation.Together, they shared the responsibility of a challenge and collaborated as peers to overcome it.A humbling aspect of SoTL is the winds that fill the sails of traditional academic hierarchies (e.g., full versus assistant professor, director of XYZ institute, number of publications) seemed to carry less weight at the ISSOTL conference.What mattered were reciprocal, "scholarly, engaged, inclusive, and collegial" (Chick et al., 2017, p. 14) conversations about teaching and learning that leveraged the range of expertise brought by faculty and
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".