Reframing the Moral Limits of Markets Debate: Social Domains, Values, Allocation Methods
Bibliographic record
Abstract
textabstractWhat should and what should not be for sale in a society? This is the central question in the Moral Limits of Markets (MLM) debate, which is conducted by a group of business ethicists and liberal egalitarian political theorists. These MLM theorists, which we will dub ‘market moralists,’ all put forward a specific version of the argument that while the market is well suited to allocate some categories of goods and services, it is undesirable for the allocation of other such categories. We argue that the current MLM debate is too much framed in terms of a market/non-market dichotomy. Moreover, authors tend to distinguish insufficiently between values such as freedom, equality, and efficiency, and allocation methods such as the market, the queue, and rationing. We introduce a new conceptual scheme consisting of societal domains, values, and allocation methods to provide a better structure for this debate. The argument is illustrated from the education and healthcare domains.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".