Brokering Boundary Crossings through the SoTL Landscape of Practice
Bibliographic record
Abstract
This study examines the lived experiences of seven internationally diverse scholars from Canada, the United States, New Zealand, and Australia to answer the question: how do we make meaning of our collective boundary crossing experiences across disciplines and positions within SoTL? Our positions range from graduate student, faculty, and academic developers, to department chair and centre director. We conducted a phenomenological study, based on narratives of experience, and drew on Wenger-Trayner and Wenger-Trayner’s (2015) theoretical framework that explores the features of a landscape of practice. Guided by this framework, we analyze our boundary crossings and brokering across the “diverse, political and flat” features of the SoTL landscape. Our collective findings highlight the critical role brokers play in facilitating boundary crossings. Brokering is precarious, bringing people together, building trusting relationships, and developing legitimacy while negotiating deadlocks, bureaucracy, authorities, and a multitude of challenges. Brokers, we found, require strength and resilience to mobilise, influence, and drive change in the landscape to transform existing practices or create new ones. We suggest that our analytical process can be used as a tool of analysis for future research about how brokers influence the SoTL landscape of practice and how brokering enhances SoTL development, support, and leadership.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.054 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".