It’s Not Just the Taking Part that Counts: ‘Like Me’ Perceptions Connect the Wider Public to Minipublics
Bibliographic record
Abstract
Many deliberative democrats herald the potential of minipublics to help improve the quality of democratic decision-making. Yet these democratic innovations present a paradox: how can the use of minipublics be perceived as legitimate by the maxi-public when most citizens cannot participate? In this article, we address this question in the context of Lafont’s argument that minipublics amount to ‘shortcuts’ in the democratic process. We challenge this argument by hypothesising that non-participants perceive minipublics to be legitimate when they perceive minipublic participants to be like them – and when they perceive politicians to be unlike them. Similarly, we expect that the relative importance of descriptive similarity will be related to the issue in question. We test our hypotheses in the deeply divided context of Northern Ireland, where a minipublic was held on the salient and contentious question of the polity’s constitutional future. Survey evidence confirms that ‘like me’ perceptions constitute a significant predictor of minipublic legitimacy perceptions. Our results have implications for the communication of minipublic features to the broader public, for the use of minipublics alongside conventional decision-making processes, and for further empirical research.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".