Forming an Intellectual Community of Practice for Scholarly Writing at a Professionally Focused University
Bibliographic record
Abstract
Scholarly writing is an expectation for faculty at many universities because this writing is intended to advance knowledge and innovation. However, many professionally focused teaching universities do not have minimal faculty requirements for scholarly publication. This is a problem because faculty at these universities can contribute to the literature with research on teaching and on the practical application of theory. As faculty at a professionally focused teaching university that does not have a “publish or perish” policy, we desired to contribute scholarly writing beyond the university’s expectations. This contribution became increasingly difficult to make on account of the fast-paced, innovative nature of our institution. Given this context, we formed a multi-disciplinary Intellectual Community of Practice, which we define as a group of intellectual practitioners who share a common goal, concern, or passion for learning through organized regular interactions. The goal of our group was to provide peer support for writing individually in our respective fields. In this article, we describe how we formed our group, how the group developed over time, and what positive outcomes have resulted from our interactions. We conclude the article with reflections on the process and recommendations to others for how to form an Intellectual Community of Practice.
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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 | 0.108 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.047 | 0.039 |
| Scholarly communication | 0.037 | 0.029 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".