Play, talk, learn : promising practices in youth mentoring
Bibliographic record
Abstract
Issue Editors Notes 1 Michael J. Karcher, Michael J. Nakkula Executive Summary 7 1. Youth mentoring with a balanced focus, shared purpose, and collaborative interactions 13 Michael J. Karcher, Michael J. Nakkula This article presents the framework for understanding the nature of mentoring interactions that helps organize the other articles in this volume in terms of their analysis of interaction focus, purpose, and authorship. 2. Mutual but unequal: Mentoring as a hybrid of familiar relationship roles 33 Thomas E. Keller, Julia M. Pryce This article presents evidence favoring a hybrid model of relationships for successful school-based mentoring interactions. 3. I dunno, what do you wanna do? : Testing a framework to guide mentor training and activity selection 51 Michael J. Karcher, Carla Herrera, Keoki Hansen This article tests hypotheses regarding the distinction between relational and goal-directed interactions and the importance of collaborative activity negotiations between mentors and mentees. 4. Beyond the dichotomy of work and fun: Measuring the thorough interrelatedness of structure and quality in youth mentoring relationships 71 Michael J. Nakkula, John T. Harris This article presents and discusses associations between match structure (guiding purposes and activity focus) and ratings of mentoring relationship quality. 5. GirlPOWER! Strengthening mentoring relationships through a structured, gender-specific program 89 Julia M. Pryce, Naida Silverthorn, Bernadette Sanchez, David L. DuBois This article describes a structured approach to supporting girls through the Big Brothers Big Sisters program that balances focus, purpose, and authorship. 6. Deconstructing serendipity: Focus, purpose, and authorship in lunch buddy mentoring 107 Timothy A. Cavell, Joye L. Henrie This article describes the particulars of lunch buddy mentoring and the reasons that it might be an effective form of youth mentoring. 7. The structure of effective academic mentoring in late adolescence 123 Simon Larose, Diane Cyrenne, Odette Garceau, Pascale Brodeur, George M. Tarabulsy This article explores the structure of the academic mentoring relationship in late adolescence through analysis of its varied experiences and mentor behaviors. 8. Building mentoring relationships 141 Stephen F. Hamilton, Mary Agnes Hamilton This first of three commentaries provides a historical perspective on the work presented in this volume. 9. Culture, context, and innovation: A Kiwi Canuck perspective 145 Dave Marshall, Karen Shaver Two leading practitioners discuss the benefits of the contributions in this volume for helping mentors working with youth in Canada, New Zealand, and other diverse contexts. 10. Structuring mentoring relationships for competence, character, and purpose 149 Jean E. Rhodes, Renee Spencer This closing commentary addresses the importance of exploring different approaches to youth mentoring and the potential impact of such approaches on youth outcomes. Index 153
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".