Institutional complexity and logic engagement: An investigation of Ontario fine wine
Bibliographic record
Abstract
We contribute to research on institutional complexity by acknowledging that institutional logics are not reified cognitive structures, but rather are open to interpretation. In doing so, we highlight the need to understand how actors engage with institutional logics and the creativity that such engagement implies. Using an inductive case study of the Ontario wine industry, we rely on the notion of scripts to explicate how actors engage with the aesthetic and the market logics that are entrenched in their field. Our findings reveal two scripts that are used to adhere to the aesthetic logic (farmer and artist) and one that is used to adhere to the market logic (business professional). We find that not only can actors enact two different scripts to adhere to an institutional logic, but also that flexible script enactment takes place within interactions with specific audiences. Thus, we found no unique match between particular logics and specific audiences, but rather that the aesthetic and the market logics, and their underlying scripts, are relevant in the interactions with each of the audience groups, albeit to varying degrees. These findings have important implications for research on institutional complexity.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".