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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".