Employment Experiences of Nigerian Immigrant women in the United States and Canada
Bibliographic record
Abstract
African immigrants come to the United States and Canada for a better life; most come for the sake of job opportunities and professional advancement. Nigerian immigrant women are one of these groups of African immigrants. While it is likely that they experienced discrimination in the workforce in Nigeria, research has shown that African immigrants, African immigrant women, and Nigerian immigrant women, in particular, experience more discrimination in their host countries. Researchers have also shown that these groups may experience discrimination based on national origin, race, gender, educational background, and sometimes even religion. However, there is a gap in the research surrounding African immigrants’ experiences with employment discrimination.There is no research on the employment experiences of specific sub-Saharan African groups, such as Nigerians, or how their experience in their new country compares to their experience in their original country. This is necessary research as it exposes the employment experiences of Nigerian immigrants, which can inform solutions to employment discrimination through governmental policies and encourage employers to work towards improving their African immigrants’ employment experiences. This research closes a small but important part of this gap by exploring the employment experiences of Nigerian immigrant working women in the United States and Canada through survey responses. Results show that although Nigerian women in the United States and Canada continue to experience employment discrimination, they have also noted factors contributing to positive employment experiences. These positive experiences have allowed them to prefer their work experiences in the U.S. and Canada to their work experiences in Nigeria.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".