Relational and caring partnerships: (re)creating equity, genuineness, and growth in mentoring faculty relationships
Bibliographic record
Abstract
Mentoring in academia has traditionally and currently been prescriptive and institutionally driven. The purpose of this paper is to deconstruct these current mentoring practices with a critical feminist stance. New understandings are shared and gained through dialogue, relevant literature, and performativity to (re)create and name a caring and relational partnership. This caring and relational partnership is grown through a process of mutuality and reciprocity, and based on relational ethics, authenticity, and solidarity. By embracing ideologies of caring and relational ethics, mentoring blurs the lines of mentor/mentee to a perpetual state of walking beside each other in equity to learn and strengthen each other's insights into our worlds. Material realities become illuminated through our shared journeys growing an appreciation and gift of the other. In turn, engaging in meaningful dialogue informs scholarship increasing our understandings of the human condition.
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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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".