Addressing Disparities: A Study of Service and Resource Gaps for BIPOC Community Members in Hamilton and the Surrounding Area
Bibliographic record
Abstract
This research investigates the challenges faced by the Black, Indigenous, and People of Color (BIPOC) community in Hamilton, Ontario, with a focus on hate crimes, social support, and access to community resources. Utilizing a mixed-methods approach, data was collected through surveys administered to twenty-five BIPOC individuals, parents/guardians of BIPOC children, and social service providers. Findings reveal a significant surge in reported hate crimes targeting specific communities, highlighting the urgent need for comprehensive action to address discrimination and promote inclusivity. Moreover, the study identifies gaps in awareness and utilization of community resources among BIPOC individuals, underscoring the importance of culturally sensitive programs and services. Internalized forms of discrimination, such as colorism and lateral violence, were also prevalent, emphasizing the need for targeted interventions to foster a sense of belonging. Mental health emerged as a top priority, signaling the necessity for culturally competent mental health services. The research underscores the importance of addressing systemic inequalities and promoting dialogue surrounding race and identity to create a more inclusive and supportive environment for the BIPOC community in Hamilton.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".