Both Sides Now: Growth Through Shared Learning in Mentoring Relationships
Bibliographic record
Abstract
There is a broad recognition of the importance and value of mentoring to the library profession, especially for early career professionals (Freedman, 2009; Irwin, 2021). In Canada many new LIS graduates often work in limited-time contract positions at the beginning of their careers, moving through different roles, different work cultures and environments, and have to navigate strategic positioning to develop a skill set that will ultimately get them permanent roles. Strong mentorship takes time - mentors trade away hours that could be used to pursue their own goals and spend them supporting someone else's, with the ultimate goal of growing the profession and supporting diverse voices and experiences. Mentors often cite strong mentorship they received and how it positively shaped not only their careers, but their professional experiences as a reason for becoming mentors themselves (Bell & Rosowsky, 2021). Over the course of a librarian's career there are many opportunities to both become a mentor, and find opportunities to be mentored. The best mentorship situations have mutual respect, trust, strong communication, shared values, and a desire to mutually learn and grow as mentor and mentee. There is generally little formal guidance on how to be a good mentor, and what does exist is largely developed outside of the library world. This said, the best of mentoring is discipline-agnostic (Havrilla, 2020). This presentation examines ideas and best practices for effective mentoring relationships, rooted in the lived experiences of the presenters with this work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".