The Changing and Challenging Environment of Nonprofit Human Services: Implications for Governance and Program Implementation
Bibliographic record
Abstract
Abstract Nonprofit human service agencies are an essential part of the social safety net and their role in many policy fields such as community care, workforce development, and disability services is growing. The funding, delivery and entire configuration of human services systems is in transition in the US, as in many other countries, albeit with great variation depending upon local and regional circumstances. Consequently, nonprofit human service agencies need to develop sustainable program and business models that are also responsive to the heightened expectations on transparency and accountability. In addition, policymakers and government officials will need to work closely with nonprofit human service agencies in order to ensure effective and efficient service delivery. Drawing on evidence from the policy and nonprofit literatures, this brief offers a set of hypotheses about the implications, and possible paradoxes, for the nonprofit sector that are likely to emerge from the increasingly competitive environment among service providers and corresponding pressure by public and private funders for more collaboration among agencies. We explore both public policy for nonprofits in human services and strategic responses by this sector, considering the first order effects designed to enable nonprofits to adapt to a reconfigured model, and the second order effects in which governments and nonprofits address the consequences of the first round. These effects are likely to vary by organizational size and by service field, resulting in quite different outcomes and relationships with government for large multi-service agencies and those in highly regulated fields such as child protection versus small nonprofits, particularly in fields such as community care with closer connections to the informal sector.
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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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".