Early Career Visible Minority (ECVM) Faculty Experiences of Mentoring in a Western Canadian University.
Bibliographic record
Abstract
Mentoring experiences of early career visible minority (ECVM) faculty members in Canadian Universities are yet to be explored. While there is substantial literature and evaluated studies on the benefits of mentoring new faculty members (Bean et al., 2014; Beane-Katner, 2014; Boice, 1992; Henry, 2010; Mullen & Forbes, 2000), few studies portray the context of Western Canadian universities. Based on the steady increase in the population of immigrants in Canada, exploring the mentoring experiences of ECVM faculty members in a Canadian University is important. This study aimed to explore the mentoring experiences of ECVM faculty members as they transition into professoriate roles in a Canadian university. \nI investigated the perceptions and experiences of ECVM faculty members at a university in Western Canada. In this study, I used an interpretive qualitative design through online face-to-face interviews with eleven ECVM faculty members. Each interview was audio recorded, transcribed verbatim, and sent to individual participants for validity purposes. Data were analyzed using NVIVO 12 software.\nFindings indicated that mentoring is practiced and conceptualized in the university; however, based on the differences related to culture, language, and backgrounds, ECVM faculty members have unique needs different from other early career faculty members. Early Career Visible Minority faculty experienced challenges with workload intensity, discrimination and stereotypic behaviours, meeting the university standards, language barriers, and adapting to the university culture. Furthermore, participants engaged in socialization and enculturation including observing faculty and students, asking questions, attending gatherings such as orientation parties and social evenings, learning more about the norms and culture, and adjusting to the university system.\nRecommendations from the study were derived with utmost emphasis on the need to make mentoring possible for all ECVM faculty. A key recommendation is that universities ought to create safe and conducive mentorship environments to address the needs of ECVM through diverse mentoring options and mentoring networks which will generate more mentoring opportunities for ECVM faculty members. \nThis study is significant to early visible minority career researchers, universities, and other educational organizations that employ visible minority individuals, and the wider society. The study will create awareness of mentoring programs, enhance better planning and management of mentoring activities, and improve the need to support ECVM faculty members.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".