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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
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 it