Mentoring from the Outside: The Role of a Peer Mentoring Community in the Development of Early Career Education Faculty
Bibliographic record
Abstract
Developing an identity as a researcher and negotiating the expectations and responsibilities of academic life are challenges that many beginning education faculty face. Mentoring can provide support for this transition; however, traditional forms of mentoring may be unavailable, limited, or lack the specific components that individual mentees desire or need. In this paper, we draw on a community of practice perspective to examine and understand the complex and emerging nature of an informal peer mentoring community composed of beginning education faculty members from different institutions. Our engagement in this peer mentoring community is examined through reflections on our experiences and our collective narratives. The formation of our group began with our mutual desires for support in advancing scholarship and navigating the transition to academia and has grown into a community that supports us both personally and professionally as we develop our identities as educational researchers.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.019 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".