The Best Deal GiIlette Could Get? Procter & Gamble's Acquisition of Gillette
Bibliographic record
Abstract
January 27, 2005, was an extraordinary day for Gillette's James Kilts, the show-stopping turnaround expert known as the “Razor Boss of Boston.” Kilts, along with Proctor & Gamble chairman Alan Lafley, had just orchestrated a $57 billion acquisition of Gillette by P&G. The creation of the world's largest consumer products company would end Kilts's four-year tenure as CEO of Gillette and bring to a close Gillette's 104-year history as an independent corporate titan in the Boston area. The deal also capped a series of courtships between Gillette and other companies that had waxed and waned at various points throughout Kilts's stewardship of Gillette. But almost immediately after the transaction was announced, P&G and Gillette drew criticism from the media and the state of Massachusetts concerning the terms of the sale. Would this merger actually benefit shareholders, or was it principally a wealth creation vehicle for Kilts? To understand the factors that persuaded shareholders of both P&G and Gillette to merge their companies, the valuation metrics involved in determining the merger consideration, compensation packages for key managers, and the politics (internal, local government, and regulatory) that impact major mergers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.230 | 0.074 |
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".