Introduction to nonprofit management : text and cases
Bibliographic record
Abstract
1. The Nonprofit Organization in Society Case 1.1 Mote Aquaculture Park: Sturgeon Project Case 1.2 Elephant Walk Through 2. Starting the Nonprofit Organization Case 2.1 Launch of Durra: Women in Islamic Banking Case 2.2 MIA, Philippines 3. Nonprofit Organization Governance and Structure 3.1 OHNo Swim Club: Organizational Governance and Mission 3.2 YMCA of London, Ontario 4. Leadership in Nonprofit Organizations Case 4.1 Good Intentions Gone Awry at the National Kidney Foundation Case 4.2 Dickinson College: Inspiration for a Leadership Story 5. Performance Measurement Case 5.1 Lake Eola Charter School Case 5.2 Otago Museum 6. Nonprofit Strategy and Change Case 6.1 Atlanta Symphony Orchestra Case 6.2 Health Care Center for the Homeless 7. Nonprofit Capacity and Networks Case 7.1 Western Area Youth Services Case 7.2 Rollins College Philanthropy and Nonprofit Leadership Center 8. Managing the People: Staff and Volunteers Case 8.1 Alice Saddy: Caring for the Community Case 8.2 Consultancy Development Organization (CDO) 9. Marketing Case 9.1 Ten Thousand Villages of Cincinnati Case 9.2 The Toronto Ultimate Club 10. Obtaining and Maintaining Organizational Momentum Case 10.1 Ontario Science Center: Agents of Change and Beyond Case 10.2 Newfoundland Centre for the Arts 11. Financial Management Case 11.1 Goodwill Industries of Greater Grand Rapids Case 11.2 Kiddyland 12. Advocacy and Lobbying Case 12.1 AOL Time Warner (A) Case 12.2 East Coast Trail Association 13. International Perspective Case 13.1 Women's Tennis Association (WTA) in Asia Case 13.2 NASSCOM 14. Social Entrepreneurship Case 14.1 Competing for Development: Fuel Efficient Stoves for Darfur Case 14.2 Care Kenya: Making Social Enterprise Sustainable
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".