Promoting Scholarship and Faculty Development through Faculty Learning Communities
Bibliographic record
Abstract
Faculty learning communities (FLCs), whether they are topic or cohort-based, are a form of professional development that promote scholarship and collegiality among faculty members. This article describes how a number of FLCs were initiated in a Faculty of Nursing (FoN). Members who participated described the FLCs as scholarly, creative and morale enhancing. One of the most significant impacts in the topic-based FLCs was having members create a scholarly product such as articles, letters, theatrical performances, books, faculty modules, briefs and paintings. For the cohort-based FLC the product was preparing pre tenure faculty for tenure. It is recommended FLCs be voluntary, include meals, ask members to commit to attending each session and have expert facilitation. This article describes several examples of FLC’s and best practices around development and facilitation for effective FLC’s.
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".