Bridging the Gap between Electoral and Participatory Democracy: The Electoral Motivations behind Participatory Budgeting in Chicago
Bibliographic record
Abstract
This paper explores the electoral dynamics of participatory budgeting projects in Chicago, IL, a topic neglected in the participatory democracy literature. Combining qualitative fieldwork with electoral data, I argue participatory budgeting is more likely to be adopted by elected officials who identify as progressive, face strong electoral competition, and are non-incumbents. These officials mobilize support for participatory budgeting to enhance their democratic legitimacy and build their constituency networks. In contrast to research focused on participatory budgeting as a non-partisan deliberative initiative, I attribute the uneven emergence of participatory budgeting projects in Chicago to the strategic electoral interests of aldermen, suggesting explanations of participatory budgeting focused on the drivers of the process should assign a greater role to electoral interests. More broadly, this research suggests approaching policy transfer as a contextually embedded process that precludes normative assumptions about particular policies absent a consideration of the institutional and social environment.
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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 | 0.003 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".