Bibliographic record
Abstract
Wal-Mart is one of the largest companies in the world. It has more annual revenue than the GDP of Switzerland. Wal-Mart is so huge that it effectively sets the terms for large swaths of the global economy, from retail wages to apparel prices to transoceanic shipping rates to the location of toy factories. Indeed, if there is one single aspect to understand about the company, it is the fact that Wal-Mart is transforming the relations of production in virtually every product category it sells, through its relationships with suppliers. But its influence goes far beyond the economy. Its construction of super-centers molds the landscape, shapes traffic patterns and alters the local commercial mix. It is the largest retailer in the United States, Mexico and Canada. Wal-Mart is also the largest private employer in the United States, Mexico and Canada and it has 1.8 million employees around the globe1. The Wal-Mart entrepreneurship story is full of simple but important truths. It is a story about principled, focused leadership that has been able to effectively and consistently balance values and the bottom line in a way that has seldom occurred. It is a story about the power of the free – enterprise entre- preneurship and indicated that how the system is the engine that drives democracy. It is a story about trust- the bases of all successful relationships. It is a story about a special man a special group of people and a special organization. It is a truly believe, a story that has positive applications for millions of people and organizations. The Wal-Mart entrepreneurs Sam Walton started to grow his own dream, with performing twelve simple principles and developed one of the largest retailing industries. Wal-Mart is one of the most powerful entities in the world. Concerning the deficiencies in managing process of commodity distribution in the developing countries and Iran, this conceptual article speculates on Wall-Mart's entrepre- neurship trends for solutions in Iranian society.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".