Bibliographic record
Abstract
Abstract: The purpose of this article is to provide an overview of priorities for evaluation capacity building in the voluntary/nonprofit sector and to raise awareness among evaluation professionals of the key issues for nonprofits that may have an effect on evaluations. The various challenges for nonprofit organizations in the evaluation of their programs, projects and activities include the availability of resources, evaluation skill levels, the design of evaluations, and the nature of nonprofit work. Among the priorities for evaluation capacity building in the nonprofit sector that emerge from these challenges are: fostering collaboration; addressing resource and skill needs; exploring methodological challenges; and building a feedback loop into evaluation. There is currently a large opportunity to open the dialogue process in evaluation, for nonprofits to work together, and for nonprofits to work with funders and evaluators to address evaluation challenges. Evaluators have a role to play in meeting evaluation challenges in nonprofit organizations by helping to find strategies for affecting change, exchanging information with the nonprofit sector community on advances being made in this area, and ensuring that efforts are sustainable.
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 | 0.321 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".