Antigal: Strategy and Succession Challenges in a Family-Owned Vineyard with Global Ambitions
Bibliographic record
Abstract
Antigal is an Argentinian winery with an integrated business model including vineyards, wine production, and distribution. Antigal is owned by the Cartoni family. Virgilio Cartoni entered Antigal as a minority shareholder in 2007, and in 2016 he and his wife Ana Maria took full control of the winery. Antigal consists of three companies. Virgilio and Ana Maria have four children: Stefano, Francesco, Alessandra, and Antonella. Since 2016, the couple each own 20% of the vehicle ROCKY, parent to two companies of Antigal, and the four children own 15% each. Virgilio and Ana Maria also own 50% each of the vehicle BACO, parent to the third company of Antigal. In mid-2018, Antigal became a multi-generational family-managed firm with Stefano, Alessandra, and Francesco working as company managers. Francesco, Virgilio, and Ana Maria’s second-oldest son started working for a Chilean producer and exporter of wines following his graduation from university, and he soon realized that the future of the wine business was in China. Consequently, in 2016 he moved to China to work for the Chilean company there. In 2018 Francesco resigned from the job to start a full-time MBA program and begin to build relationships for his family business. Francesco, along with his brother and sister, has a vision to bring Antigal to the next level. There are many challenges ahead for the company to overcome: How to structure the family business governance? How to expand the business and increase the enterprise value of the firm? How and how much could China contribute to it?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".