Experiences of Engineering Education Research Faculty Members in Canada: A Collaborative Autoethnography
Bibliographic record
Abstract
As Engineering Education Research (EER) capacity has grown in Canada, tensions have emerged including negotiating legitimacy and the struggle to find space in institutions. In Canada, these tensions have been examined through the perspectives of graduate students studying EER, however, the experiences of new faculty members in EER-specific roles in our region are under-researched. In this paper, we examine our experiences as pre-tenure faculty members in EER-specific roles using collaborative authoethnography. Through a series of reflective prompts and iterative discussions, we address the question of how early-career EER faculty negotiate building an academic “home.” Our findings highlight how our senses of belonging in our academic unit, our engineering faculty, and the wider EER national community are mediated by our abilities to take on engineering identities. This research can help other individuals and faculties understand what supports can help us survive and thrive in Canadian academic contexts.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.038 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".