Inter-cultural mentoring for newcomer immigrants: Mentor perspectives and better practices
Bibliographic record
Abstract
The objective of this study was to draw from mentor feedback and reflections and examine the practices of mentors successful in mentoring immigrant newcomers. The paper reports on how mentors related the competencies they reported as foundational for decoding, absorbing, and transferring tacit/explicit knowledge holdings. Capturing rich insights, the guidelines for best practice are presented for mentoring of immigrant newcomer mentees in smaller/medium cities (SMC) with emerging immigrant populations. Findings identify seven key themes by mentors: mentees’ culture, mentors’ cultural self-awareness, building relationality and accessibility, sponsorship, deep learning, racism, and small city truths as they influence (a) knowledge transfer and personal learning within the dyad, (b) acculturation/adaptation, and (c) perceived business and network gains on the part of the mentee. This paper also petitions for clarification of the multiple meanings accorded to the use of inter-cultural mentoring (ICM). A purposeful sampling strategy and best practice research (BPR) were employed for this research investigation.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".