Coordinated selection of collective action: Wealthy-interest bias and inequality
Why this work is in the frame
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Bibliographic record
Abstract
We extend a collective action problem to study policy and project selection by heterogeneous groups who prefer to work together on a joint initiative but may disagree on which initiative is best. Our framework, adapted from a model of multiple threshold public goods, presents groups with several mutually exclusive projects, any of which require sufficient support from the group to succeed. Individuals strictly prefer to contribute where and how much they believe others expect of them to ensure joint project success. Groups tend to coordinate on the public good preferred by the wealthiest member, demonstrating a wealthy-interest bias even without corruption, politics, and information asymmetries. At the same time, groups divide costs in highly progressive ways, with the wealthy voluntarily funding a disproportionate share, helping offset the inherent inequality from endowment and selection differences. We discuss applications for policy selection, charitable giving, and taxes.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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