Bibliographic record
Abstract
This foreword introduces the importance and content of the book. The first paragraph reads: As Jane Austen almost wrote: It is a truth universally acknowledged that a nonprofit in possession of an important mission must be in want of individual donors. Yet despite the essential role that private individuals play in supporting every type of nonprofit activity across the world, our understanding of what these donors need and want – and just as crucially what they don’t want – in order to gain and sustain their support, is often sadly lacking. The collective value of donations from individuals far outstrips the value of funding from corporations and philanthropic foundations (collectively known as ‘institutional donors’) by a ratio of 3-to-1, yet these latter sources of voluntary income get far more attention from scholars, practitioners and the general public. This is partly for practical reasons: institutional donors may only provide around a quarter of total nonprofit funding but they create a paper trail which makes them much easier to find, to quantify, to study and to pass comment on. They might have a website, issue press releases and have staff with easily accessible contact details. Institutional donors are often keen to be visible because their motivation includes achieving business benefits or securing legitimacy and the right to operate. Such is the domination of institutional philanthropy in the public imagination that when philanthropy, is being discussed - and especially when it is being criticized - what commentators and critics usually have in mind is foundations and corporations not individuals.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.471 | 0.475 |
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".