Bibliographic record
Abstract
Abstract The ‘limits of markets’ debate broadly concerns the question of when it is (im)permissible to have a market in some good. Markets can be of tremendous benefit to society, but many have felt that certain goods should not be for sale (e.g., sex, kidneys, bombs). Their sale is argued to be corrupting, exploitative, or to express a form of disrespect. In Markets without Limits , Jason Brennan and Peter Jaworski have recently argued to the contrary: For any good, as long as it is permissible to give it for free, then it is permissible to give it for money. Their thesis has led to a number of engaging objections, and I leverage recent work on the nature of feasibility within political philosophy to offer a new challenge. I argue that feasibility offers a constraint on which markets can be permissibly implemented. Though it may be possible to create a morally acceptable version of an otherwise repugnant market, some of these markets may be infeasible, and so we are not permitted to implement them. After laying out this challenge, I consider several replies. They concern the relevance of feasibility, and whether any markets really are infeasible. This provides an opportunity to explore the dangers of pursuing the infeasible and with markets generally. I conclude by considering what might lead us to pursue these markets despite their infeasibility, or how knowledge of infeasibility may prove useful regardless.
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.
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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".