Speaking of the Sacred: Exploring Religion, Spirituality, and the Boundaries of Emotional Communities through Oral History
Bibliographic record
Abstract
Drawing on Barbara H. Rosenwein’s work and the history of emotions more generally, this article considers the significance of emotional boundaries within the context of oral history interviews. I examine how, in my own interviews with Irish Canadians in Winnipeg, interviewees navigated these unspoken social codes when they told stories of religion and spiritual experience. Widely shared understandings among the emotional communities of Canadian society, the Irish community in Winnipeg, and the interview itself meant that some stories about spiritual experiences required an invitation while others did not. When interviewees discussed landscape and nature, they drew freely on spiritual language to relay their experience of the sublime within particular spaces. Discussion of religion, however, tended to focus on its historical and political significance within the Irish community, unless an invitation was offered to broaden the emotional boundaries of our conversation. I argue that as oral historians and co-creators of these boundaries within the interview space, it is important to consider the boarders of the emotional communities that shape our interviews. If we combine oral history methodology with the work of historians of emotion and religion, we may open up new possibilities to examine the understudied role of faith and religion in interviewees’ lives.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.043 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".