Citizens influencing public policy‐making: Resourcing as source of relational power in e‐participation platforms
Bibliographic record
Abstract
Abstract E‐participation platforms create spaces and opportunities for participation and collaboration between governments and citizens. This paper aims to investigate the role of power on formal e‐participation platforms and digital spaces that are controlled by the governments. Although those types of platforms have been increasing in numerous countries, they have been criticised as often leading to a lack of or decrease in citizen engagement. We propose a relational view that examines how power is related to the use of resources in practice, that is, to resourcing. To explore this issue, we examine citizens' participation on three urban mobility platforms in three major Brazilian cities. Our study makes two main contributions. First, we contribute to the literature on e‐participation by explaining how a relational view of power helps to understand the nature and consequences of citizen participation in public policy‐making. Second, we integrate the concept of resourcing as both a source and constitutive element of relational power. We propose a process‐based model of resourcing as power that opens the black box of resourcing through the identification of three distinct phases in time: resourcing IN, resourcing WITHIN and resourcing OUT.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".