Experiences of Black Evangelicals in Predominantly White Evangelical Churches in Calgary
Bibliographic record
Abstract
In Canada, there is a dearth of research on Black experiences in the Christian evangelical church. Using a narrative qualitative methodology and undergirded by a critical race theory (CRT) theoretical framework, I explored the experiences of 5 Black evangelicals in predominantly White evangelical churches (PWECs). I focused on the challenging experiences they go through in PWECs, how they have responded to these challenging experiences and the factors that contributed to the choice of attending PWECs. Participants had to identify as Black and had to have been members of a PWEC in Calgary for at least a year. Through semi-structured interviews, I explored their experiences with them and the themes that emerged suggest that Black evangelicals go through a host of challenging experiences such as racism and racial microaggressions, and the lack of meaningful relationships in PWECs. As a result of these experiences, Black evangelicals have devised a host of strategies in response to these challenging experiences. Further, in spite of these experiences, it emerged that all the participants preferred attending PWECs for various reasons, one of the most important being the problems they perceived within the Black churches. These generally align with what is found in the broader literature, and the experiences of these Black evangelicals provide one instance of how Black people in Canada navigate life in a racialized society.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.017 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".