<b>The Tyranny of the Minority: </b><b>How Democracy Defeats Itself and the Case for Algorithmic Authority as a Replacement</b>
Bibliographic record
Abstract
This paper reconsiders the long-standing debate over the efficacy and sustainability of democracy as a system of governance. It advances two related arguments. First, it identifies the Tyranny of the Minority as democracy’s primary mechanism of failure, an inversion of Tocqueville’s classical concern with majority rule. Second, it proposes a theoretically superior, though politically improbable, alternative in the form of an algorithmic model of governance capable of embodying the moral architecture envisioned by John Locke and John Rawls, and operationalized through the application of a Pareto-Minimax function. The central contention is that democracy, though historically unprecedented in its emancipatory potential, is now a critically endangered political form. Its decline is not merely contingent upon external pressures from authoritarianism but arises from an inherent structural flaw that may be termed the population paradox. The population paradox lies at the heart of democratic self-rule. Democracy assumes that populations possess the cognitive and moral competence to select leaders who will act in their collective interest. Yet this assumption is empirically untenable. If electorates lack the capacity to discern competence from manipulation, democracy becomes self-undermining because the system depends on the very judgment it cannot reliably produce. The paradox, then, is epistemic as much as political. How can individuals rationally decide what is best for them when they lack the intellectual means or motivation to make informed, collective choices?
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".