Bibliographic record
Abstract
La vida en Shanghai ha sido mas que comoda para Mike Graves, el gerente general de un joint venture entre una empresa estadounidense de vestuario y un fabricante chino. Sus hijos van al mejor colegio, vive en un agradable barrio para extranjeros y su empresa paga chofer y ninera. Mike ha transformado al joint venture en un gran exito, al menos a ojos de sus ejecutivos chinos y funcionarios de gobierno locales. Zhong-Lian Knitting ha rescatado a tres empresas que perdian dinero y ha aumentado su planilla de 400 a 2.300 empleados. Pero el jefe de Mike, el CEO de la empresa en los EE.UU., Heartland Spindle, no comparte esa vision optimista. ?Un ROI de 4% es pesimo?, dice. ?Los numeros ya deberian ser mejores?. El quiere un retorno sobre la inversion de 20%, lo que signifi ca despedir a 1.200 trabajadores chinos. Tambien quiere apuntar al nivel alto del mercado del vestuario, lo que implica que el joint venture debera alcanzar estandares de calidad mucho mas exigentes de los que ha podido cumplir hasta ahora. Para empeorar las cosas, los ejecutivos chinos quieren llevar a cabo una cuarta adquisicion, la que dejaria al joint venture en condiciones de lanzar su propia marca de vestuario. Pero es una movida que podria perjudicar las utilidades durante anos. ?Podra Mike impedir que el joint venture se desplome? Cuatro comentaristas ofrecen sus consejos expertos en este caso de fi ccion: Eric Jugier, presidente de Michelin (China) Investment en Shanghai; Dieter Turowski, director ejecutivo de fusiones y adquisiciones de Morgan Stanley en Londres; David Xu, un director de McKinsey en Shanghai; y Paul Beamish, director del Asian Management Institute en la Richard Ivey School of Business de la University of Western Ontario en Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.097 | 0.015 |
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".