2014 Special Events Report: U.S. and Canada
Bibliographic record
Abstract
Special events are important to many nonprofit organizations because they help raise awareness of a cause and help cultivate relationships with donors and potential donors. Events tend to be more costly than other fundraising strategies in terms of return on investment, but they are often incorporated into overall fundraising strategy because they provide visibility for the organization and opportunities to involve people in its activities. The literature on special events is largely focused on anecdotal descriptions of how-to's for producing events -- budgets, checklists, timelines, descriptions of types of events, formats, use of media for events, managing volunteer involvement, securing in-kind support, and evaluation of the event. What is missing from the literature is benchmarking research that would help a nonprofit determine whether an event is appropriate considering its circumstances, how its event results compare with those of other like organizations, and effective ways to follow up with constituents, media and potential donors after the event. In this study, the AFP Foundation for Philanthropy collected information on event planning and management to enable nonprofit managers to compare their events with those of other organizations by type, size, region, metropolitan area size, and number and types of events per year. The study results provide a tool to help nonprofits make informed decisions about whether to invest in an event, how to plan a successful event(s), steps to maximize return on investment, and follow-up activities to help turn event attendees into donors. Study results will also be used by the Association of Fundraising Professionals (AFP) to plan formats for presenting event planning information to its members.
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) | 0.001 | 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) | 0.000 | 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".