Bibliographic record
Abstract
Scholars typically suggest that deliberation, defined as communication guided by reason‐giving and inclusion, works best behind a veil of ignorance or when personal differences are bracketed. In this article we explore deliberation within ethnically diverse groups. We operationalize ethnicity in three ways: as an aspect of individual identity, as an identity that is made salient through priming, and as an enactment relative to interactions in particular situations. In this way, we can explore the applicability of our previous experimental results to ethnically diverse groups. We find similar results: within ethnically diverse groups, deliberation matters; participants are more likely to reconsider their positions when deliberating than when simply talking about politics. Ethnicity has no adverse effects on the quality of deliberation, indicating that bracketing has no significant impact. On the contrary, when conceptualized as a relational enactment, ethnicity is correlated with increased levels of reason‐giving and inclusion, and hence higher quality deliberation. This suggests deliberation works in multiethnic groups in much the same way as—if not better than—it does in homogeneous groups. Deliberation is a robust form of political communication that not only helps manage, but also embraces diverse subjective experiences as a part of the political 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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".