The Power of Mentoring Black CEGEP and University Students
Bibliographic record
Abstract
Aim/Purpose: This article takes a deep dive into the positive and long-lasting effects of mentoring Black CEGEP and college students through an autoethnographic approach where I share my life experience relating to mentoring and representation. In my attempts to relate to and understand the educational pathways of Black students in Quebec and the United States, I conducted a deep dive into my educational journey since I have experience being a student in both education systems. Background: The hypothesis for this text is that Black persons who receive continuous mentoring throughout their CEGEP and college careers have a higher chance of graduating college and attending a graduate studies program than those who do not receive mentoring. Methodology: The use of autoethnography as a qualitative research method arose to create a connection between my educational experience, the state of many Black students in Quebec’s educational system and the possibility of what their lives could become if matched with caring mentors. The use of autoethnography allowed me to deeply self-reflect on the intersections between my educational pathway without and with mentoring and the current research findings regarding the trend of Black students who do not receive consistent mentoring. Findings: Regarding the educational pathway and experience of Black students in Montreal, there is a recurring theme—the lack of grace and communication of options for the future regarding Black students. This paper will deeply dive into the subject. Impact on Society: The power of mentoring has been linked to self-efficacy and retention. There are clear benefits to creating mentoring programs to help CEGEP and university students learn leadership skills, get acquainted with post-secondary education, and fall in love with the continuous journey of academic and personal growth and elevation.
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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 | 0.014 | 0.027 |
| 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 | 0.023 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| 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".