Bibliographic record
Abstract
INTRODUCTION TO THE IVEY CASEBOOK SERIES PREFACE ACKNOWLEDGEMENTS INTRODUCTION Chapter 1: M&A Responsibilities, Roles, Duties, Governance Husky Energy Incorporated Conagra, Inc. A Guide for Directors: How Directors Can Navigate the Potential Problems of M&As Chapter 2: M&A Strategy Formulation - Motivations and Justifications Hewlett-Packard in 2001 Newell Company: The Rubbermaid Opportunity MapQuest, Inc. CIBC-Barclays: Should Their Caribbean Operations be Merged? Chapter 3: M&A Valuation and Evaluation Empire Company Limited: The Oshawa Group Limited Proposal Gillette Company's Acquisition of Duracell International Inc. - Cost of Capital FirstCaribbean: The Proposed Merger Chapter 4: M&A Approach & Structuring Bluewater Foods Corporation Merger of Nova Corporation and Transcanada Pipelines Ltd. Accounting for Acquisitions at JDS Uniphase Corporation Chapter 5: M&A Execution CNCP Telecommunications Buyout Accura/Flexco Acquisition National IR: Catalyst Investments Acquisition of Montreaux Chapter 6: M&A - Closing the Deal Cemex: The Southdown Offer BC Sugar Refinery, Limited Antitrust: A Threat to Mergers and Acquisitions? Chapter 7: M&A Integration and Value Realization Information Systems at FirstCaribbean: Choosing a Standard Operating Environment FirstCaribbean International Bank: The Marketing and Branding Challenges of a Start-Up Harmonization of Compensation and Benefits for FirstCaribbean International Bank Chapter 8: M&A Avoidance and Alternatives Blue Jay Energy and Chemical Corporation IPC Corporation, Singapore ATS Inc. Chapter 9: After the Merger: Recoveries and Exits Daimlerchrysler: Post-Merger News Gillette's Energy Drain (A): The Acquisition of Duracell Call-Net Enterprises Inc. (A) Chapter 10: Cross-Border M&A Bombardier Transportation and the Adtranz Acquisition Vincor and the New World of Wine ABOUT THE EDITORS APPENDIX: Additional Cases Available from Ivey Publishing
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".