Curating the past and the struggle for authenticity of Canadian whisky producers
Bibliographic record
Abstract
While authenticity is often treated as inhering in people or objects, it is a socio-symbolic accomplishment, requiring authenticity work. Some organizational scholars have begun to study the use of historical discourses in authenticity work, but the historical narratives have tended to be treated as readily accessible and available for activation, as needed. In contrast, we argue that such authenticity work consists of constructing history in a manner that endows the historical narratives with moral and emotional force that enables a shared sense of goodness and virtue between organizations and key audiences. We report the results of an inductive study of the Canadian whisky industry, which investigated the challenges associated with recreating Canadian whisky tradition after decades of neglect. We also report how distilleries and Canadian whisky advocates curate the past in attempts to challenge solidified perceptions of the category and of individual organizations. Our findings reveal two facets of curating work: restorative facet involves attempts to faithfully adhere to the material practices that have been utilized in the past; reimaginative facet involves the faithfulness to the past but eroded ideals and values through reinvention of material practices. The manuscript advances the performative view of authenticity and contributes to the literature on strategic use of the past.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.058 | 0.039 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".