Towards a Context-based Citizen Participation Approach
Bibliographic record
Abstract
Governments are more and more adopting Citizens' participation processes since they may help better understand needs and better reach communities' goals. With the emergence of ICTs, citizens' participation processes took new forms such as social media, blogs and participative platforms. Nevertheless, although citizens' participation outstands a great value for governments, its implementation raises several issues. Based on a review of the literature, this paper identifies a typology of issues regarding the implementation of CP processes. This paper is an attempt to open new research avenues on citizens' participation through the multidisciplinary typology of issues it proposes. As well, drawing on two categories of issues (citizens' and technology issues), a context-based citizen participation approach is proposed that is based on three main concepts: context-based reasoning, spatio-temporal representation, and visual analytics. Further empirical studies need to be established to assert the relevance of the proposed approach.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".