The Impact of Emotional Toll and People of Color (POC) Spaces on the Experiences of Black University Students
Bibliographic record
Abstract
Aim/Purpose: This article explores how studying in a white educational setting acts as motivation for Black people to engage in Black spaces. Background: When considering the impact of being Black in white educational spaces and the additional effort that is required of these students just for being there, it is important to examine the strategies employed to resist and cope with the challenges faced. Methodology: Data was collected over a 2-month period using a quantitative online survey with Qualtrics. 52 (n = 52) Black university students currently enrolled in a Canadian university were surveyed. To analyze the data, a mix of descriptive statistics, regression analysis and ANOVA were used. Findings: The data reveals that emotional toll is an important factor to consider when talking about attending white educational spaces. Moreover, the study points to how emotional toll plays a part in the motivation of Black students to seek out Black/ People of Color (POC) spaces. Impact on Society: This study along with the underrepresentation of Black students in academe, and the challenges they face while attending these educational settings provides an insightful look into the experiences of Black students.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".