From Learning Comes Meaning: Informal Comentorship and the Second-Career Academic in Education
Bibliographic record
Abstract
Informal mentoring relationships develop out of mutual identification and the fulfillment of career needs. As new faculty, we struggled to balance and decipher all the various facets inherent in the research, service, and teaching responsibilities in our new roles. This paper chronicles an informal comentorship collaboration we struck up to support our efforts as second-career academics in the field of education, seeking to navigate our way through institutional resocialization at a mid-sized Canadian university. Using a collaborative autoethnographic approach, we collected data comprising handwritten notes, tape-recorded coversations, e-mail reflections, and metareflections crafted after scheduled meetings over the course of a single academic school year. We sought to link theory with practice while using our own stories, narratives, and lived experiences as a basis for understanding our respective journeys toward social health and well-being in the academy, as well as our proficiency and competence as new scholars. From our analysis, we were able to interpret more clearly our roles, responsibilities, and needs, as well as institutional and departmental culture and norms. We offer practical implications and five lessons we have learned regarding the use of informal comentorships as an approach to managing the institutional resocialization of second-career academics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".