The Changing Size Distribution of California's North Coast Wineries
Bibliographic record
Abstract
Abstract In this paper, we use three different goodness-of-fit tests for log-normality in conjunction with kernel nonparametric density estimation methods to examine both the size distribution of California North Coast wineries over time and by age. Our kernel density estimates indicate that the size distribution of wineries has changed from positively skewed to bimodal. These results are inconsistent with those in other industries, but are consistent with recent empirical research in the wine industry, which finds that smaller firms are comprising a larger component of market share. In terms of the distribution of firm size by age, our results indicate that as wineries age, the size distribution of firms becomes less skewed and more bimodal, which is also inconsistent with the research on other industries which finds that as firms age, the size distribution becomes more normal. Our results indicate that unlike other industries, where entry is very difficult, small firms can enter the wine industry and survive. (JEL Classifications: L11, L22, L25)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".