The Role of Self-Interest in Deliberation: A Theory of Deliberative Capital
Bibliographic record
Abstract
How do successful deliberations unfold? What happens when they unravel? In this article, I propose that we think of the dynamics of participant engagement within deliberation as series of self-interested and reciprocal investments in and divestments from deliberative capital . This article has three parts. First, I draw on the literatures on deliberative democracy and social capital to outline a theory of deliberative capital. I highlight the important role self-interest plays in the process of those initial investments – instances of engagement in positive deliberative behaviours. Second, drawing from my experience as a facilitator, I give an account of the particular indicators of investments and divestments that we might expect to see in a given deliberative engagement. Third, I briefly outline two innovative facilitation techniques that can be utilized at the beginning or during a deliberative process that trigger self-interest, which incentivizes investments and discourages divestments.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".