Evaluation of E-Participation Efficiency with Biodiversity Measures - the Case of the Digital Agenda Vienna
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We introduce the Effective Number of Issues measure for e-participation efficiency. This novel index is based on the Shannon entropy measure of biodiversity and summarizes the amount of information gained through an e-participation project in one number. This makes the comparison between different e-participation projects straightforward and lays the foundation for the rigorous analysis of success factors of e-participation projects in a data-driven way. After providing the formula and rationale for the new measure we use the ENI index to benchmark the idea generation process for the digital agenda Vienna against other projects. It turns out that the efficiency of this project is significantly higher than those observed for other cases. We conjecture that this can be attributed to the user-friendly design of the software platform and the effective communication strategy of the process management. Finally, suggestions for further research are given.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it