Are you mentoring or coaching? Definitions matter
Bibliographic record
Abstract
© The Author(s) 2020. New library and information science professionals, without previous experience in information organizations, are often left adrift, unsure how to apply theory or best practice to a new work environment. To bridge this gap, mentoring and coaching opportunities are often employed (or mandated) to provide new practitioners with required skills, knowledge, or networking. There are opportunities to harness implicit and explicit learning through experiences and interactions through mentoring and coaching. Definitions of mentoring and coaching in the profession are often used interchangeably when discussing the growth and development of an individual. This leads to the following questions: How do librarians define both mentoring and coaching? How do mentoring and coaching relate to professional development? To address the research question, 47 semi-structured interviews were conducted with librarians in Canada, New Zealand, and the United States between 2015 and 2016. Participants were asked about their mentoring and coaching experiences. During the interviews, participants were asked questions about their experiences as a mentor or mentee. In addition, participants were asked to define both “mentoring” and “coaching.” The authors used an inductive approach to data analysis, and interviews were coded by category.
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 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.002 |
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; both teacher heads agree on what is shown here.
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".