Nonprofits and Volunteers in North Dakota Communities
Bibliographic record
Abstract
This study looks at non-profit organizations in North Dakota to see how they attract and keep volunteers, how volunteer hours can be used to draw funding to nonprofits, and the increasingly important role non-profit groups play in local economies by creating jobs, services, and acting as a voice for those who might not be heard otherwise. Results show how universities can support local communities by developing research data nonprofit groups can use to solve practical problems, by acting as a bridge between student volunteers and appropriate organizations, and by creating a journal or online site communities can use to connect with each other and as a resource for information. This paper examines the following questions: which kinds of volunteer work do the people in North Dakota consider beneficial? What do residents consider to be the main benefits of volunteer work in U.S. society? What do they consider to be the main problems and challenges associated with volunteer work in U.S. society? The research used qualitative methods. The study made use of focus group data and panel discussions analysis published in academic journals. The research concluded that the main issues confronting volunteer organizers are limited resources, and the need for information and knowledge about volunteer efforts in North Dakota. In these circumstances community engagement centers serve as vital sources of news and information. They can also serve as a prospective mobilizing platform for volunteer organizations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".