Which conception of political equality do deliberative mini-publics promote?
Bibliographic record
Abstract
In democratic political systems, political equality is often defined as an equality of opportunity for influence. But inequalities in resources and status affect the capacity of disadvantaged citizens to achieve an effective political equality. One common thread running through recent democratic innovations is the belief that appropriate institutional devices and procedures can alleviate the impact of background inequalities on the presence and voice of the disadvantaged within those designs. My objective is to achieve a clearer understanding of the conception of political equality that informs a specific subset of these designs: deliberative mini-publics. I focus firstly on the methods of participant selection advocated to secure equal presence. According to what principle is participation distributed? If it is according to the ‘equal probability’ principle, rather than ‘equal opportunity’, what difference does this make in terms of political equality? Secondly, achieving equality of voice is usually conceived in terms of equalising opportunities for influence among participants. How is this objective understood and what does this say about the underlying conception of political equality?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".