The Role of Self-Interest in Deliberation: A Theory of Deliberative Capital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it