Learning Experiences and Challenges facing Black International Students at the University of Windsor
Bibliographic record
Abstract
In the past two decades, the international-student population increased to about 600,000 (Canada Bureau for International Education, 2018). According to The Immigration, Refugees, and Citizenship Canada (IRCC, 2019I), international students contributed an estimated $21.6 billion to the Canadian gross domestic product. With the COVID-19 pandemic, recruitment of international students, and the economic contribution they bring is under threat. More so, the lockdown imposed by the government, and schools’ adoption of online learning, further poses challenges and unique experiences to children, and young persona, especially international students. We used qualitative data from a focus group of 10 male Black students, aged 20 years and above, attending the University of Windsor in Ontario, Canada. In addition, we include the experiences and concerns of a student in Nigeria. The findings show that students face a number of social and environmental factors that negatively impact their online learning experiences. These factors include: economic support from parents/guardians, availability and access to learning resources, the place of residence, and lack of academic support from instructors, administration, and peers. We conclude that many Black students feel dissatisfied and stressed by the lack of support and how they have been neglected during COVID-19. These experiences are likely to impact their mental health severely.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".