Bibliographic record
Abstract
Introduction Citizens’ lack of knowledge is often used as an argument against their participation in policymaking (for example, Schumpeter, 1943). How can we expect citizens to deliberate if they lack information, feel disinterested in politics and are unable to convey coherent policy preferences (Achen and Bartels, 2016)? Compared to politicians and lobbyists, citizens spend little time thinking about politics. They have little access to information beyond what is available in the media. For democratic participation to flourish, it is important to bridge the knowledge gap between citizens and policymakers. Bridging that gap is one of the purposes of DMPs. Central to their design is the opportunity for citizens to think, reflect, listen to each other and engage with the range of evidence presented to them. In this way, mini-publics can help address the cognitive challenges of modern citizenship (Warren and Gastil, 2015). Learning takes place both between DMP participants themselves, and through the provision of structured learning materials. It can be easy for DMP organizers, who put great effort into writing briefings and organizing programmes of witnesses, to forget the importance of peer-to-peer learning. However, such learning is vital: DMP participants often speak of how much insight they gain from hearing about the lives and perspectives of people very different from themselves. The development of such mutual understanding is at the core of good deliberation. Our focus in this chapter, however, is on the learning that is structured and enabled by DMP organizers. Research shows that briefing materials and interactions with subject-matter experts help to explain much of the participants’ learning in mini-publics (Setälä et al, 2010). Acquiring knowledge and deliberating with their peers based on credible evidence enables citizens to reach a considered judgement. Thus, evidence, as discussed in this chapter, refers to written and oral expert information, as well as arguments and personal testimonies by advocates and stakeholders who are invited as witnesses to a mini-public. In most mini-publics, evidence is given in the form of briefing materials and witness testimonies. While evidence gathering is an essential part of all DMPs, practices vary in terms of the selection and presentation of evidence in deliberation. Concerns are often raised over how sponsors and organizers of mini-publics might use expert evidence to manipulate the deliberative process.
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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 | 0.049 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 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".