Ongoing Challenges Faced by Expatriate Managers: An Exploratory Study of Expatriate Managers in Nigeria and Canada
Bibliographic record
Abstract
This qualitative study will seek to uncover the concept of workforce diversity, to discover the effects of workforce diversity on an organization, to find out how the managers manage diverse workforces, to highlight the challenges faced by expat managers in managing a diverse workforce, and to identify the possible solutions to these challenges. Additionally, the study will compare the results of these aims between the workforces of Canada and Nigeria. Four research questions were developed to help guide the research, and are: (1) What is workforce diversity? (2) How does a diverse workforce impact an organization? (3) How do the managers manage a diverse workforce? (4) What are the challenges experienced by expat managers in managing a diverse workforce? The research questions will be addressed through the analysis of data that will be collected by interviewing 8 expat managers (three in Canada and five in Nigeria). Analysis of the data will include both phenomenology (to determine the themes and phenomena inherent to the managers lived experiences) and a comparative case study (to compare the results between the two countries). Chapter 1 provided an introduction and overview of the study. Chapter 2 discusses information regarding both Nigeria and Canada. Chapter 3 contains the literature review for this study. Chapter 4 describes in greater detail the research methodology to be used. Chapter 5 presents the data and the analysis of the data and contains the conclusions of the research, a discussion of the results, and a summary.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".