Configurations of Digital Participatory Budgeting
Bibliographic record
Abstract
Participatory budgeting is a democratic innovation increasingly supported by digital platforms. Like any technology, participatory budgeting platforms are not value-free or politically neutral; their design, configuration, and deployment display assumptions and configure participant behaviour. To understand what kinds of configurations occur and what kinds of democratic values they hold, we studied 31 digital participatory budgeting cases in Spain, France, and Finland. These cases were all supported by the same technical platform, Decidim , allowing us to focus on the variations in their configurations. We examined the data from these cases and identified 25 different technical configurations and 15 participatory budgeting configurations. The configurations observed in our cases exhibit individual and community-centred assumptions about expected state-society interactions, as well as open vs managerial approaches to participatory budgeting. Based on these findings, we highlight a dilemma for civic technology designers: to what degree should platforms be open to configuration and customisation, and which political values should be enforced by platform design?
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".