Bibliographic record
Abstract
Objective The conversations in accountability were designed to gain an understanding of the use and changes to accountability in charities over time, including learning how results are measured. Methods As part of a larger study which investigated accountability in charities working to reduce mortality of children younger than 5 years in least developed countries, a multiple-case study comprising semistructured in-depth key informant interviews was conducted to investigate the use and effects of accountability in three charities of differing sizes. Results Smaller charities tend to use fewer accountability mechanisms than larger ones, whereas the variation in their use between small and medium-sized charities is greater than the variation between medium-sized and large charities. Conclusion Although accountability has changed over time, charities believe that they are providing the correct amount of accountability – that is, enough to satisfy the perceived demands of their stakeholders but not so much that it detracts from the mission or incurs costs in excess of benefits. However, the tools to determine effectiveness and impact are lacking.
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 | 0.048 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.056 | 0.024 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".