Globally responsible leadership : managing according to the UN Global Compact
Bibliographic record
Abstract
Foreword - Georg Kell Introduction - Joanne T. Lawrence, Paul W. Beamish Acknowledgments About the Editors About the Contributors PART I. BACKGROUND 1. Responsible Business: A Brief Perspective - Joanne T. Lawrence 2. The Business of Business Is (Responsible) Business - Danial Malan 3. The United Nations and the Transnational Corporations: How the UN Global Compact Has Changed the Debate - Andreas Rasche 4. Context and Dynamics of the UN Global Compact: An Idea Whose Time Has Come - Sandra Waddock PART II. THE TEN PRINCIPLES OF THE UN GLOBAL COMPACT 5. Human Rights as Ethical Imperatives for Business: The UN Global Compact's Human Rights Principles - Florian Wettstein 6. Our Role as Managers in Understanding and Fulfilling the Labour Principles of the UN Global Compact - Michael J. D. Roberts 7. Embedded Sustainability and the Innovation-Producing Potential of the UN Global Compact's Environmental Principles - David Cooperrrider, Nadya Zhexembayeva 8. The Challenges of Corruption in Business, Government, and Society - Peter Rodriguez PART III. CASE STUDIES: THE TEN PRINCIPLES IN PRACTICE Human Rights Killer Coke: The Campaign Against Coca-Cola - Henry W. Lane, David T. A. Wesley Google in China - Deborah Compeau, Prahar Shah Ethics of Offshoring: Novo Nordisk and Clinical Trials in Emerging Economies - Klaus Meyer Talisman Energy Inc. - Lawrence G. Tapp, Gail Robertson Labour Netcare's International Expansion - Saul Klein, Albert Wocke Nestle's Nescafe Partners' Blend: The Fairtrade Decision (A) - Niraj Dawar, Jordan Mitchell Jinjian Garment Factory: Motivating Go-Slow Workers - Tieying Huang, Junping Liang, Paul W. Beamish Textron Ltd. - Lawrence A. Beer Bayer CropScience in India (A): Against Child Labor - Charles Dhanaraj, Oana Branzei, Satyajeet Subramanian L'Oreal S.A.: Rolling Out the Global Diversity Strategy - Cara C. Maurer, Ken Mark Huxley Maquiladora - Paul W. Beamish, Jaechul Jung, Joyce Miller Staffing Wal-Mart Stores, Inc. (A) - Alison Konrad, Ken Mark Environment RBC--Financing Oil Sands (A) - Michael Sider, Jana Seijts, Ramasastry Chandrasekhar Barrick Gold Corporation--Tanzania - Aloysius Newenham-Kahindi, Paul W. Beamish Host Europe: Advancing CSR and Sustainability in a Medium-Sized IT Company - Rudiger Hahn Veja: Sneakers With a Conscience - Oana Branzei, Kim Poldner Canadian Solar - Paul W. Beamish, Jordan Mitchell Scandinavian Airlines: The Green Engine Decision - Jennifer Lynes Anti-corruption Phil Chan (A) - Paul W. Beamish, Jean-Louis Schaan Medical Equipment Inc. in Saudi Arabia - Joerg Dietz, Ankur Grover, Laura Guerrero Governance Failure at Satyam - Ajai Gaur, Nisha Kohl PART IV. APPENDIXES: EXCERPTS FROM THE UN GLOBAL COMPACT WEBSITE Appendix A. The Corporate Commitment Appendix B. The Communication on Progress Report Appendix C. Leadership Blueprint: Blueprint for Corporate Sustainability Leadership Within the Global Compact Appendix D. UN Global Compact Communication on Progress and the Global Reporting Initiative (GRI)
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".