Whole Person Mentoring for All Stages of the Library Career
Bibliographic record
Abstract
The newly launched McGill Library Mentoring Program has two defining features: it is inclusive and holistic. It was designed not only to reach archivists and academic library staff at every career stage, but also to encourage them to participate in the program as both mentors and mentees in order to share their unique interests. For example, many new professionals are entering the workforce with coveted skills in information and education technology. While they may benefit from being mentored by their more experienced colleagues, they may also participate in the program as mentors so that others may benefit from their skills and knowledge. The holistic, or whole person approach to the design of the program offers mentoring on a range of topics that move beyond professional development and the promotion and tenure process, into issues of personal development and work-life balance. This paper describes, in detail, the mentoring program that was developed in less than one year by an ad hoc committee with administrative support. It includes the steps required to participate in the program as a mentee or mentor, training materials, and lessons learned. Grounded in best practices, the design is flexible and sustainable and is transferable to other environments for those that are considering or rethinking mentoring at their own libraries.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".