<i>e</i>‐democracy and participatory decision processes: lessons from <i>e</i>‐negotiation experiments
Bibliographic record
Abstract
Abstract e‐Democracy takes place at different levels, ranging from local to regional to state governments. It also takes different forms: voting, consultation and the participation in the construction of the alternative course of actions. This paper is concerned with the use of information and communication technologies in participative e‐democracy at community and local government levels. It postulates that to design participating systems the needs of the potential users must be determined and models of decision‐making and conflict resolution that can be used by lay people need to be constructed. A general framework for the design of systems for participatory decision‐making is presented. The experiences with the design and deployment of the Inspire e‐negotiation support system, its use by a large number of people from many countries, and the results of studies of the users and the use of Inspire are presented. Based on these experiences, an example of the implementation of the general framework is given. The paper also stresses the need for the development of aids and materials for lay people who wish to educate themselves in participating in e‐democratic processes. Copyright © 2004 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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".