Bibliographic record
Abstract
Introduction to the Text i Acknowledgements ix UNIT 1 Moral Navigation: Business Ethics & Society 1 1.1 Tools for the Journey 2 Spotlight: A Hijacking Ethics Codes: Apple Google 1.2 The Moral Compass: Integrity in Business 18 Spotlight: Up in the Air Michael Clayton Case: The Tylenol Case Ethics Code: Johnson & Johnson 1.3 Show Me the Money: Greed is Not Good 38 Spotlight: Wall Street Big Men Cases: Martha Stewart's Insider Trading Costco's Tainted Berries Ethics Code: Costco 1.4 Talk to Me: The Impact of Technology 57 Spotlight: Her The Net Disconnect Cases: Edward Snowden and the NSA Target's Data Breach Target's Online Tracking Ethics Code: Yahoo! UNIT 2 Moral Leadership: Ethical Theory 76 2.1 Aerial Surveillance: Ethical Theory 77 Spotlight: The Insider 2.2 The Ends Justify the Means: Teleological Ethics 96 Spotlight: Contagion Park Avenue: Money, Power, and the American Dream Blue Jasmine Case: The Ford Pinto Ethics Code: Ford Motor Company 2.3 Duties Rule: Deontological Ethics 117 Spotlight: Shattered Glass Arbitrage Quiz Show Food Inc. Cases: Beech-Nut's Apple Juice Minute Maid Lemonade Foster Farms' Chickens Ethics Codes: National Public Radio (NPR) Dole Food Company, Inc. 2.4 Moral Character: Aristotle's Virtue Ethics 136 Spotlight: Erin Brockovich Roger & Me Salmon Fishing in the Yemen Cases: Kellogg's and Michael Phelps The European Horsemeat Scandal Ethics Code: GM's Corporate Citizenship Kellogg's K Values 2.5 The Caring Community: Feminist Ethics 156 Spotlight: The Company Men Erin Brockovich Under the Clear Blue Sky Case: Film Recovery Systems, Inc. Ethics Code: Microsoft UNIT 3 Moral Reflection: Thorny Questions 177 3.1 Finding the Balance: Addressing Environmental Disasters 178 Spotlight: Civil Action Local Hero Cases: WR Grace Co. West Virginia Chemical Spill Exxon Valdez Ethics Code: Exxon Mobil Corporation 3.2 Going Postal: Addressing Workplace Violence 194 Spotlight: John Q Polytechnique Murder by Proxy: America Goes Postal Cases: Gunman at LAX The Montreal Massacre Virginia Tech Rampage Ethics Code: Los Angeles World Airports (LAWA) 3.3 Stand By Me: Addressing Workplace Inequities 213 Spotlight: Made in Dagenham Matewan Cases: UAW and H&M on Pay Equity The Fast Food Workers Strike H&M and Child Labor Marriage Equality Ethics Code: H&M 3.4 Enough Already: Addressing Workplace Harassment 230 Spotlight: North Country The Invisible War Disconnect Cases: Eveleth Mines Bullies in the NFL Ethics Code: Amazon.com 3.5 Working for Change: Global Justice & Human Rights 245 Spotlight: Sleep Dealer, Darwin's Nightmare, Fires of Kuwait Cases: Bangladesh's Sweatshop Collapse, Coca-Cola in Colombia, Pfizer in Nigeria, Dunkin' Donuts Ad Campaign Ethics Code: Dunkin' Donuts 3.6 Transformation: The Art of Personal Power 263 Spotlight: Groundhog Day Invictus Cases: Kudumbashri's Work Skills Program Ethics Code: The Coca-Cola Company APPENDIX 4.1 Movies in this Book 277 4.2 Cases in this Book 278 4.3 Ethics Codes Cited in this Book 279 Index
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".