Mentorship for Change: A Look at Four Inner-city Jamaican Boys’ Journey to Positive Masculine Practices
Bibliographic record
Abstract
Abstract Research data in Jamaica shows that boys from inner-city areas are disproportionately at risk throughout their youthful years and are more likely to engage in hyper-masculine behaviors that reinforce stereotypes (Crawford, 2010; Lewis,2007; Plummer, 2014). While hyper-masculine practices have become a trademark in some of these areas, not many opportunities are provided for young men to engage in alternative masculine practices. This study will investigate the impact of a 6 - week mentorship program on four boys’, age 14-17 from single parent household, developing hyper-masculine behaviors to determine changes in practices. The boys will participate in a series of workshops and integration activities that reinforce and immerse them in positive masculine behaviors throughout the 6 weeks. Through interviews and video diaries they will share their journey with the researcher and three other mentors. Preliminary results suggest three of the boys are likely to change their practices, while one is concerned that deviation from some traditional masculine practices in his community might isolate him. The study aims to construct new knowledge to better assist educators and government entities to develop better strategies to engage boys from inner-city communities so they can achieve greater success in their lives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".