Bibliographic record
Abstract
Introduction DMPs should be consequential. Participants who experience taking part in a mini-public may find the exercise valuable in its own right, but without impact outside the process, DMPs are at risk of becoming insignificant talking shops that do little to enhance the quality of collective decision-making. This, indeed, was one of the early concerns raised against DMPs. For Carole Pateman (2012: 9), their reach was limited, they had little influence in decision-making and the public did not know a lot about them (see also Rummens, 2016). Fast-forward to a decade later and, today, DMPs are increasingly becoming visible in public life (see OECD, 2020). They are commissioned by national leaders like President Emmanuel Macron in France or parliamentary committees in the UK. They are part of the global environmental group Extinction Rebellion's core demands. Belgian political party Agora won a seat in the Brussels Parliament by running on the single issue of calling for a citizens’ assembly. Similarly, editorials in publications like The Financial Times , The Guardian and The Economist recognize the merits of DMPs. As the popularity of DMPs grows, the concern shifts from their insignificance to the implications of giving power to an unelected, randomly selected group of individuals. At the heart of this issue are concerns about the legitimacy of DMPs. To what extent should DMPs shape decision-making? Should DMPs be empowered to make binding decisions? Are they better off taking an advisory role? What is the basis of DMPs’ legitimacy in the first place? These issues, among others, point to the challenge of finding the sweet spot of ensuring that DMPs are neither too powerless, nor too powerful. This chapter examines this challenge in three parts. We begin by establishing the premise that before any mini-public should seek to influence decision-making, it should first establish its internal legitimacy. While there is no established consensus on what count as ‘legitimate’ DMPs, we can draw on a range of literature that defines what counts as good deliberation in mini-publics. We are cautious that before any calls for mini-publics’ consequentiality are made, it is necessary to first establish whether the procedure was run with integrity and demonstrated good-quality deliberation. We then turn to the second section and consider what makes DMPs legitimate from the perspective of non-participants. We draw on the growing empirical work on this topic.
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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 | 0.058 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 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".