Bibliographic record
Abstract
Many mentorship programmes pair more experienced elders with trainees, enabling experienced practitioners to pass knowledge down to younger generations. Professor Kayoko Watanabe, Aichi-Shukutoku University, Japan, believes in the importance of mentoring programmes and has been investigating mentoring programmes. The idea of mentoring programmes has yet to gain traction in Japan and Watanabe helped implement and continues to play a role in improving the Hiroshima City Youth Support Mentor System, which was launched in 2004 by the Board of Education in Hiroshima City and connects school-aged children with volunteers who act as mentors. Watanabe believes the theory and practice of mentoring programmes are interconnected, working together in a feedback loop to improve mentoring programmes. She has been studying the current status and core issues surrounding the mentoring movement in the US, UK, Germany, Canada, Australia and New Zealand and uses a number of theories in her work, including lifelong development, social capital and social investment. Watanabe has evaluated the mentoring programmes, considering the viewpoints of mentors, mentees and parents of mentees and found a clear recognition of the benefits of the programme for all stakeholders.
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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 | 0.125 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".