Emotions Uncorked: Inspiring Evangelism for the Emerging Practice of Cool-Climate Winemaking in Ontario
Bibliographic record
Abstract
This paper examines how organizations create evangelists, members of key audiences who build a critical mass of support for new ways of doing things. We conduct a longitudinal, inductive study of Ontario’s cool-climate wineries and members of six external audience groups who evangelized on behalf of their emerging winemaking practice. We found that wineries drew from three institutionalized vinicultural templates—“provenance,” “hedonic,” and “glory”—to craft rituals designed to convert these audience members. These rituals led to inspiring emotional experiences among audience members with receptive gourmand and regional identities, driving them to engage in evangelistic behaviors. While a growing body of work on evangelists has emphasized their individual characteristics, the role of emotions in driving their activities, as well as how they advocate for organizations, our study demonstrates how evangelism can be built through ritualized interactions with organizations. Specifically, we reveal how organizations develop rituals that translate emerging practices into inspiring emotional experiences for particular members of audiences. This suggests that rituals can be used not only to incite dedication within organizational boundaries, but to inspire members of external audiences to act as social conduits through which emerging practices spread.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".