Social Capital and Mentoring: Rethinking Mentoring with a Decolonized Perspective
Bibliographic record
Abstract
Mentoring programs that provide newcomers with information about Canadian work norms and culture have the potential to positively impact the integration of skilled immigrants into the Canadian lab our market. Past research on immigrant integration has highlighted the benefits immigrants receive from mentorship programs, such as support, empathy, encouragement, counseling and friendship, collegiality, and career satisfaction. Less is known about the benefits that mentors receive from these programs. This highlights a crucial gap in understanding the reciprocal benefits of mentorship and its impacts on mentors. This paper shares findings from a research study on “Facilitators and Barriers to Mentorship Programs for Newcomers to Canada” to highlight the benefits mentors gain in the form of social capital and, in doing so, to emphasize the need to decolonize existing mentorship programs and incorporate Indigenous values in mentorship practices. A decolonizing agenda is crucial to address historical and ongoing systemic inequities and to promote inclusive and equitable mentorship practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.072 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".