Knowledge is power: why public knowledge matters to charities
Bibliographic record
Abstract
The nonprofit sector is grappling with public perceptions of trust, accountability, and transparency. From the perspective of the sector, public ratings of accountability and transparency are often lower than expected, and as a result, the public's trust and its ensuing support may be dwindling. What appears to be missing from these discussions, however, is the role of knowledge. Drawing on theories and frameworks from consumer behavior and advertising, this study anticipates knowledge will moderate the effects of trust, transparency, and accountability on public support. Using telephone survey data from 3853 members of the Canadian public, the findings of this study demonstrate that knowledge does in fact have a moderating effect—for those respondents who reported high levels of knowledge about the sector, measures of trust, accountability, and transparency reliably predicted donation amount and volunteer status over and above the effect of demographic variables. For those respondents who reported low levels of knowledge, donation amount and volunteer status were predicted by demographic variables alone. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.019 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 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".