Accountability Online: Understanding the Web-Based Accountability Practices of Nonprofit Organizations
Bibliographic record
Abstract
Nonprofit organizations are increasingly using Internet-based technologies to address accountability. This article presents a set of conceptual, theoretical, and empirical innovations to help understand this phenomenon. First, this article presents a conceptual framework that delineates two key dimensions of Web-based accountability practices: disclosure and dialogue. It then posits a four-factor explanatory model of online accountability incorporating organizational strategy, capacity, governance, and environment. Last, it tests the model through a content analysis of 117 U.S. community foundation Web sites combined with survey and financial data. The descriptive statistics show that the Web site has been more effectively used to provide financial and performance disclosures than to provide dialogic mechanisms for stakeholder input and interactive engagement. Our multivariate analyses, in turn, highlight capacity- and governance-related variables, especially asset size and board performance, as the most significant factors associated with the adoption of Web-based accountability practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".