Bibliographic record
Abstract
1. 3M: Cultivating Core Competency. 2. A-1 Lanes and the Currency Crisis of the East Asian Tigers. 3. AMD vs Intel: Competitive Challenges. 4. Boeing: Redefining Strategies to Manage the Competitive Market. 5. Carrefour in Asia. 6. Dell: From a Low Cost PC Maker to an Innovative Company. 7. Ford Motor Company. 8. Jack Welch and Jeffrey Immelt: Continuity and Change in Strategy, Style and Culture at GE. 9. The Home Depot. 10. China's Home Improvement Market: Should Home Depot Enter or Will It Have a Late-mover (Dis)advantage? 11. Huawei: Cisco's Chinese Challenger. 12. ING Direct: Rebel in the Banking Industry. 13. JetBlue Airways: Challenges Ahead. 14. Lufthansia: Going Global, but Howto Manage Complexity? 15. Microsoft's Diversification Strategy. 16. Nestle: Sustaining Growth in Mature Markets. 17. PenAgain: An Entrepreneur Seeks the Holy Grail of Retailing. 18. PSA Peugeot Citroen: Strategic Alliances for Competitive Advantage? 19. Sun Microsystems. 20. Teleflex Canada: A Culture of Innovation. 21. Tyco International--A Case of Corporate Malfeasance. 22. Vodafone: Out of Many, One. 23. Wal-Mart Stores, Inc. 24. WD-40 Company: The Squeak, Smell, and Dirt Business.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.027 |
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".