Caffeine, communities, and crises: An examination of the networks, discourse, and operational strategies of contemporary specialty coffee retailers
Bibliographic record
Abstract
This dissertation uses a mixed methods approach to examine the field of independent coffee retailers from two metropolitan Canadian cities. I arrange these projects into three discrete (but overlapping) publishable papers as chapters to demonstrate how 1) hierarchies can emerge within niches of the specialist contingency; 2) context affects discourse in a cultural field dominated by in-person interactions; and 3) retailers react to and employ innovative operational strategies amidst exogeneous shocks to the niche. Taken together, this dissertation research showcases the relationships among independents in the specialist contingency, and that despite attempts to collectively mobilize the market, there remains realized disparities among retailers in localized fields with little cooperation among firms further exacerbated by the pandemic. The first chapter examines the structure of relationships among independent retailers in the specialist contingency. I use a multiplex retailer awareness matrix and blockmodeling to map the overall structure of the field of independent coffee. This chapter contributes to understanding how multiple types of ties among retailers affect market structure, and the impact of field-specific variables on firm positions within hierarchies. The second chapter focuses on the importance of context and interaction within the specialty coffee field when it comes to baristas and consumers. I use an ethnographic approach to model both in-person and online coverage of four core specialty coffee events. This chapter contributes to understanding discursive attempts at collaborative market driving and how medium affordances alter how consumers and retailers communicate in a field dominated by in-person interactions. The final chapter assesses the effect of the COVID-19 pandemic on retailers within the field, paying close attention to their initial reactions, operational strategies, and post-pandemic survival. I use pre-existing network data from the first chapter, along with social media posts and follow-up interviews, to evaluate the role of cooperative behaviour among retailers, and their use of operational accommodations to meet the challenges of ever-changing health and safety standards. This chapter contributes to understanding how existential threats to niche markets affect cooperation among firms, and how organizational attributes, network variables, and strategies to cope with the pandemic correlate with retailer survival.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".