"Lemeh Check See If Meh Mask on Straight": Examining How Black Women of Caribbean Descent in Canada Manage Depression and Construct Womanhood Through Being Strong
Bibliographic record
Abstract
Black experiences in Canada are diverse and made more complex by specific issues brought on by gender and even health. This paper seeks to examine how Black Caribbean descent women in Canada manage depression and construct womanhood through the discourse of "Being Strong." "Being Strong" (Schreiber et al., 2000) assumes that strength is naturally inherited and embedded within the cultural context of Black Caribbean descent women in Canada. The cultural construction of strength may also be derived from the historical role of Black women established during slavery and colonization (hooks 1984; Hill-‐Collins 2000). We argue that "Being Strong" may also be women's reactions to systemic and institutionalized injustices such as racism, immigration processes, and discrimination that are oftentimes overlooked in multicultural nations such as Canada. This paper examines how some Black Caribbean descent women in Canada enact and accept "Being Strong" through the maintenance of unwavering strength; and it also explores the dangers of adopting and accepting such roles. It also seeks to explore linkages between racism and mental health. Utilizing qualitative research data, this paper will give voice to a largely marginalized group, while also exploring the possibilities for more inclusive mental health services that will adequately address the needs of this group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".