Exploring the potential and limits of digital tools for inclusive regulatory engagement with citizens
Bibliographic record
Abstract
Over the past decade, independent regulatory agencies like competition authorities, water and energy regulators have increasingly turned to citizen engagement, including via digital channels. In this study, we seek to shed light on the potential and limits of economic regulators' digital engagement with citizens, compared to traditional, non-digital equivalents. More specifically, we analyse the costs and benefits of four prominent (digital) engagement tools in relation to inclusion, focusing on three key challenges for inclusive citizen engagement: (i) access, (ii) accessible information, and (iii) support in making contributions. Furthermore, we assess the technical, social, and organisational conditions under which the use of the tools can be more inclusive. We conclude that ‘turning digital’ has important advantages for inclusive regulatory engagement but is no panacea. Yet, whilst some challenges cannot be unilaterally tackled by regulators, there is considerable room for these organisations to raise the inclusiveness of their engagement, both by combining tools and modes of engagement, and by expanding their toolbox.
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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 | 0.046 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.031 | 0.032 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".