Bibliographic record
Abstract
About the Author. Preface. Getting Started. About This Book. Introduction. Chapter 1. Escalating Price Tags. Legacy Gift And Naming Rights. Acquisition Targets In The Private Sector. Private Sector Escalating Price Tags. NHL Arena Naming Rights. Pro Sports Stadiums Naming Rights Chart. National Basketball Association . The British Are Coming, The British Are Coming. Chapter 2. Nonprofit Sector Naming Rights. Higher Education and Named Gifts. School of Business. Naming Rights at Business Schools Come with the Ask. Legacy Gifts to name other schools. Benchmarking Named Gifts to Universities. University & College Athletics. Private Schools. Religious Schools. Health Care Sector. Healthcare Naming Rights offerings. Arts & Culture Naming Rights. Comparing Named Gifts in the Arts. Interview with Paul Schervish, Director, Center on Wealth and Philanthropy, Professor, Department of Sociology at Boston College. Chapter 3. Changing Strategies for Length of Term Naming Rights (Nonprofit Sector). Bundle Up--Bundle Out. New York Philharmonic Orchestra. Multi-Tier Choices of Endowed Chairs in the Arts & Culture sector. Donors like the tunes as Endowment grows: 59% Solution offers Multi-Year choices. The 59% Solution? Growing the Endowment a capital idea. Multi-Year Choices for Major Gift Donors. Limited Term Naming Rights Private Sector. Nonprofit Sector. Sunset effect. Annual Naming Rights. Chapter 4. Higher Level of Involvement and Expectation from Donors. Boosting brand name with stadiums & sponsorships. Microsoft. McDonalds. Recent Corporate Building Naming Deals. Branding discussion with Robert Passikoff (President of Brand Inc.). Chapter 5. Corporate Naming Rights Aim to Boost Brand. Boosting brand name with stadiums and sponsorships. Microsoft. McDonalds. Recent Corporate Building Naming Deals. Branding discussion with Robert Passikoff (President of Brand Inc.). Chapter 6. Nonprofits Jump the Queue to Acquire Naming Rights. Chapter 7. Legacy Gifts Mount Up in Billion Dollar Campaigns. Legacy Gifts Given Just for the Sake of Making the Gift. Chapter 8. Changing Attitude in Setting the Price Tag for Naming Rights. 51% of Historical Cost. Benchmarking. Manager's Toolbox - How to develop a Benchmark Report. Step One. Step Two. Step Three. Step Four. Existing Facilities. Chapter 9. Using the Internet to Market Naming Rights. Shopping List Technique. Descriptive Text Approach. Campaign Videos on the internet. Recognition for sponsors. Chapter 10. Spread of Naming Rights. Naming Rights Offered for sale. Canada's Emerging Trends in Naming Rights. Other recent Naming Rights Deals in Canada. Epilogue. Risk Analysis. Appendices. Appendix A. Named Stadiums Around the World as of September 2007. Appendix B. Endowment Gifts Comparison Table. Appendix C. United States Named Facilities. Index.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".