Community of Practice for Early-Career Teaching-Stream Faculty Members: Structure and Reflections
Bibliographic record
Abstract
Although a passion for education is a precursor to being hired as teaching stream faculty, many early career professors’ having little experience or training in conducting pedagogical research. To ease this transition and build a supportive community, we created a Community of Practice (CoP) book club centred around the International Handbook of Engineering Education Research. Through a collaborative autoethnography methodology, members were asked to reflect on their experience as new teaching faculty and the impact of the CoP. The preliminary data suggests that the CoP provides its members with resources to effectively adapt their expertise to engineering education research. Moreover, the CoP member’s reflections describe the additional benefits of a dedicated space and sense of community to allow for the discussion of challenges associated transition to academia. This work-in-progress paper demonstrated the value of a CoP for engaging faculty, promoting innovative teaching practices, and developing a community of support.
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 | 0.028 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".