Cause-marketing for nonprofits : partner for purpose, passion, and profits
Bibliographic record
Abstract
Foreword, Carol Cone. Acknowledgments. Introduction: My Journey, Partner for Purpose, Passion, and Profit. PART I: THE CAUSE-MARKETING MOVEMENT. Chapter 1. The New Corporate-Nonprofit Engagement. Cause Marketing: A Turning Point in Corporate-Nonprofit Relationships. An Essential New Link for Corporate-Nonprofit Engagement. Achieve Mission, Generate Revenue, and Other Benefits. Value of Cause Marketing. Cause-Related Marketing Internationally. Conclusions. Chapter 2. Integrating Value and Values. Cause Marketing Defined. Cause Marketing Is Marketing, So a Few Vital Facts. Trends Driving Cause Marketing. Corporate Drivers. Nonprofit Drivers. Conclusion. Chapter 3. Evolution of Cause Marketing. Evolution of Cause Marketing. Sales Phase. Customer Loyalty Phase. Branding Phase. Corporate Social Responsibility Phase. Nonprofit Driven Branding. Conclusion. PART II: CAUSE-MARKETING INITIATIVES: THE SEVEN P'S: BEST PRACTICES CASE STUDIES. Chapter 4. Cause-Marketing Products. Product Sales. Purchase Plus: Making Giving Easy. Licensing: Using Nonprofit Logos, Brand Identities, and Assets. Conclusions. Chapter 5. Cause-Marketing Issue Promotions. Finding the Synergistic Fit. Conclusion. Chapter 6. Cause-Marketing Programs. Cobranded Events. Cobranded Programs. Social (Public Service) Marketing Programs. Conclusions. PART III: GETTING IT RIGHT: FRAMEWORK FOR SUCCESS. Chapter 7. Creating a Cause-Marketing Orientation: Cause Preparedness. Determining Organizational Goals and Assets. Platform: The Big Simple Idea. Determine Targets for Cause-Marketing Approach. A Lucky Internally Prepare and Align the Organization. One Last Thing About Causes. Conclusion: The First Step Is Creating a Cause-Marketing Orientation. Chapter 8. Building the Cause-Marketing Program: Collaboration, Combining Assets, Creating Value. Build the Cause-Marketing Program. Collaboration: Strategic Partner Alignment. Combine Assets and Aim for Maximum Benefit. Creating Value: Determine for Both Partners. Conclusion. Chapter 9. Implementing the Cause-Marketing Program: Execution and Corporate and Community Outcomes. Execution and Outcomes. Implementing the Cause Program. Execute: Relationship Management and Delivery. Communicate: Internally and Externally. Cause-Marketing Goals Achieved: Community and Corporate Outcomes. Conclusions. PART IV: MAKING IT HAPPEN: BEST PRACTICES CASE STUDIES. Chapter 10. National Organizations: American Heart Association and First Book. Building the Cause-Marketing Program. Implementing the Program. Cause Goals Achieved. Final Thoughts and Advice. Dr. Seuss The Cat in the Hat Challenge Promotional Cause-Marketing Initiative. Building on a Cause-Marketing Orientation. Building the Cause-Marketing Program. The Big Simple Idea-with Turnkey Execution! Implementing the Cause-Marketing Program. Corporate and Community Goals Achieved. Chapter 11. Local Organizations: Food Bank (New York City) and Canadian Cancer Society (Vancover Island Region, British Columbia and Yukon District). NYC BANK-TO-BANK PARTNERSHIP: COBRAND CAUSE-MARKETING PROGRAM INITIATIVE. Creating a Cause-Marketing Orientation. Building the Cause-Marketing Program. Implementing the Cause-Marketing Program. Cause-Marketing Goals Achieved. Creating a Cause-Marketing Orientation. Building a Cause-Marketing Program. Implementing the Cause-Marketing Program. Goals Achieved. Chapter 12. Cause-Marketing Principles and Cautions: Seven Golden Rules, Seven Deadly Sins. Principles: The Seven Golden Rules of Cause Marketing. Cautions: Seven Deadly Sins. Conclusion. Final Thoughts. Partner for Purpose, Passion, and Profits. The Way Forward.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.128 | 0.066 |
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".