Bibliographic record
Abstract
ANNUAL FUND PROGRAMS ARE THE BUILDING BLOCKS for every other major funding program of the academic library.Keys to library annual fund success include identifying the library's constituents, developing a compelling case for support, and determining how the solicitation message can be delivered most effectively subject to existing internal and external barriers.Examples of effective strategies used to overcome these barriers are provided from U.S. and Canadian academic libraries.With the annual fund program firmly established, the academic library is then well positioned to seek the major funds required for growth and innovation. WHYANNUAL FUNDS?Annual fund programs are frequently called the foundation or the base of the giving pyramid.It can be tempting to focus solely on major gift programs at the middle of the pyramid or to singularly contemplate the vast intergenerational transfer of wealth awaiting those who focus on planned giving at the top of the pyramid.However, "Pyramids," as Greenfield (1999) observed, "are built from the bottom up" (p.98).So essential is annual giving to the total well being of the development program that Rosso (1991) has called the annual fund "the cornerstone and the key to success for all aspects of the resources development program" (P.51).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.364 | 0.194 |
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".