Bibliographic record
Abstract
How much inequality does market interaction generate? The answer to this question partly depends on the level of competition among economic agents. Yet, in their normative analysis of the market, theories of distributive justice focus on individual characteristics such as talents as determinants of income, and tend to ignore structural features such as competition. Economists, on the other hand, dispose of the conceptual tools to assess the distributive impact of competition, but their analysis is usually limited to allocative efficiency. Part I of the article distinguishes my argument from conventional perspectives on income inequality and redistribution. Whereas the latter propose either to redistribute income once the market interaction has taken place or to adjust the initial holdings of market participants, I focus on the distributive impact of the institutional structure of the market itself. Part II outlines the ways in which various forms of competition affect distribution. My objective here is descriptive in nature, but shows that a normative evaluation of the market has to take seriously the distributive impact of competition. This impact can be broken down into the analysis of three overlapping groups of economic agents, namely consumers, workers, and capital owners. Consumers potentially gain from competition in the form of lower prices, but these gains are only realized if competition does not put pressure on their work income at the same time. Unless competition squeezes profits unusually hard, capital owners tend to benefit from competition.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".