Bibliographic record
Abstract
We live in a turbulent and contradictory world, where there are few certainties and change is constant. In addition, over time we increasingly come to realize that much of what we think we see around us can, in reality, be something entirely different. We require greater perceptual accuracy just as the horizons become increasingly cloudy. Business cycles are becoming more dynamic and unpredictable, and companies, institutions, and employees come and go with increasingly regularity. Much of this uncertainty is the result of economic forces that are beyond the control of individuals and major corporations. Much results from recent waves of technological change that resist pressures for stability or predictability. And much results from individual and corporate failures to understand the realities on the ground when they pit themselves against local institutions, competitors, and cultures. Knowledge is definitely power when it comes to global business and, as our knowledge base becomes more uncertain, companies and their managers seek help wherever they can find it. It is the thesis of this book that a major part of this knowledge base for managers rests on developing a fundamental, yet flexible, understanding of how business management works in different regions of the world. More specifically, our aim is to develop information and learning models that global managers can build upon to pursue their careers and corporate missions. As managers increasingly find themselves working across borders, their list of cultural lessons – do's and don'ts, must's and must not's – continues to grow.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.510 | 0.314 |
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".