Uncovering the Cultural Dynamics in Mentoring Programs and Relationships
Bibliographic record
Abstract
Although cultural issues have a powerful influence on the failure and success of mentoring programs and relationships, there is scant research on this area and little in the way of guidelines that practitioners can use to help assure mentoring success. This book seeks to expand our knowledge and understanding of this topic and to foster the use of this information to enhance practice and research. The book is unique in a number of ways and will be an important resource for all those engaged in mentoring endeavors and for those conducting research in this area. First, it presents research findings on the cultural impact of mentoring at the individual relational level, at the organizational level, and within the structures of the society. Secondly, the chapters describe mentoring from an international perspective including programs from Africa, Australia, Canada, Finland, India, Ireland, Korea, Scotland, Sweden and the United States. Third, the book is research based and yet, can be easily applied to practice. Chapters provide information on lessons learned and also include reflective questions to enable the reader to delve more deeply into the constructs and findings in order to apply them to their own practice and research. This makes the book an ideal resource for training mentors and mentees, for designing mentoring programs, for teaching about mentoring, and for establishing and maintaining mentoring relationships. It also will be of value to those who are engaged in conducting research on how to create and maintain successful mentoring relationships and programs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".