Bibliographic record
Abstract
The contexts in which nonprofits operate in the US, UK, Canada and Australia are undergoing significant change. This chapter assesses the implications of three sets of recent developments in: the regulatory regimes; philanthropy and social finance; and contracting for social services. The actual public benefit and impact of philanthropy and charities is under increased scrutiny, and this is being embedded into regulatory and financing systems. Although these countries have strong traditions of philanthropy, it has become more reliant on older and High Net Worth donors so that most nonprofits must work harder for their income. The Millennials are starting to shake up the brand loyalty of the Boomer generation and have differing expectations for engaged, inclusive work environments. Social finance is widening the gap between the nonprofits with the sophistication and financial acumen to take advantage of these new tools and those that lack the capacity to do so. This division is reinforced by changing models of service delivery, which are also creating greater pressures for collaboration and mergers. To manage these major shifts nonprofits need to pay even greater attention to leadership, organization governance and more effective sector self-regulation.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".