Niche logic spillover to mainstream markets: how small players changed the mature Canadian wine field
Bibliographic record
Abstract
This thesis examines how small players change mature organizational fields. Mature fields are characterized by stable pre-existing institutional logics, interlocked relationships between participants, and high levels of practice standardization. In this thesis, I look at the emergence, diffusion, and legitimation of new, niche practices and their embodied logic within the Canadian wine field in the past 30 years. The first phase of this thesis provides a theoretical framework of how niche practices spill over to mainstream markets in mature organizational fields. The second phase is an inductive, theory-building effort to specify how small players change mature organizational fields. Based on 49 interviews with winery representatives, industry experts, and other stakeholders in the industry, I develop theory on the endogenous and exogenous forces that facilitate the emergence, diffusion, and institutionalization of a quality standard and appellation system, the Vintners Quality Alliance (VQA), which was introduced by small players in the Ontario wine field. Within this study, I explore the strategies used by small players to diffuse their new practices, as well as how current field structures affect the institutionalization of these practices. Furthermore, I consider forces that influence the persistence of the VQA in its early institutionalization phase and its existence as one logic within the field. Finally, the third phase of my thesis includes two quantitative studies examining how entrepreneurial strategies and exogenous forces influence the diffusion of new practices and new identity symbols associated with small players within the Canadian wine industry. The insights in this thesis have important implications for theory for a number of reasons. First, I consider how small players' practices and/or identity symbols diffuse and/or become institutionalized within a mature field in a more systematic manner than that found in past literature. Second, this thesis considers the co-existence of multiple logics within a field. To date, theorists exploring institutional change have focused on the transition from one dominant logic to another within fields. Third, my thesis explores the complexities involved with institutional or symbolic isomorphic change within a mature field by considering both exogenous and entrepreneurial forces of change.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".