Facing post crisis challenges through leadership and inclusion: The case of Napa Valley
Bibliographic record
Abstract
The years 2010 through 2020 brought difficult challenges for the Napa Valley, a wine region in the heart of California, representing 4% of its vineyard land and 30% in economic value. Napa experienced an earthquake in 2014, wildfires in 2017 and 2020 and then, like everywhere, was hit by the pandemic. Consequently, both the production and the sale of wines were impacted with a form of relentlessness which terribly affected the entire local sector. In addition to the devastating effects of fires and smoke on some vineyards, the health crisis came to question a well-installed business model in Napa where wines are sold directly to consumers. Here, 68% of the wineries produce less than 50,000 bottles/year and it is not uncommon to see a winery sell 2/3 of its production directly from the estate. Two other observations complete the table with a paradigm shift: an aging of Napa wine consumers, mainly American, and the social evolution of a valley which attracts the wealthy classes of people and challenges the housing needs of not just vineyard workers, but also intermediate professionals. In 2020, Napa Valley Vintners (NVV) initiated a vast strategic reflection in order to question the conditions of its development and its competitiveness to the face of these developments. The 550 wineries from the valley were invited to participate in thinking about the values of Napa, its leadership and the priorities to reconsider for the years to come. Collective commitment, leadership programs for high potentials, action local communities, diversity, integration of minorities … The list is long and it is enlightening to see how a wine region has been able to take advantage of a crisis situation to adjust its modus operandi with current issues, strengthen its leadership and create an inspiring new chapter for this world-class wine region.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".