Interest Groups: A Survey of Public Choice Thinking
Bibliographic record
Abstract
Montreal. He may be reached at Filip_Palda@enap.uquebec.ca.Research on interest groups goes back at least to Cicero. He distinguished parties (partes) from factions (factio). A faction worked in its own interests whereas a party sought to discover the common good. Since then only the details of inquiry into interest groups have changed. Two thousand years on, the question of what political systems promote good interest groups still excites and maddens researchers. Thinking on this question sputtered until Montesquieu and later Madison explained how government institutions could be structured to limit the power of factions. Madison, in his #10 Federalist paper, argued that factions could be contained by dividing power between competing branches of government and by encouraging factions to be numerous so none would come to dominate government. The greatest minds in political economy have focused on interest groups in order to understand how power flows, just as Boltzman and the atomists of the mid-19 th century focused on the atom as a mental device for understanding thermodynamics. De Tocqueville, Marx, Toynbee, Orwell; the names go off like cannons. These thinkers sought to build a science of power, but their writings as those of most political sages of
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".