Where MLM Intersects MFA: Morally Suspect Goods and the Grounds for Regulatory Action
Bibliographic record
Abstract
The market failures approach (MFA) to business ethics argues that economic theory regarding the efficient workings of a market can generate normative prescriptions for managerial behaviour. It argues that actions that inhibit Pareto optimal solutions are immoral. However, the approach fails to identify goods that should be regulated or prohibited from the market, something common to the moral limits to markets (MLM) approach to business ethics. There are, however, numerous assumptions underlying Paretian efficiency, including some about the preferences of market participants. Trade in some goods violates some of these assumptions, and so these goods are morally suspect and can be understood to indicate that the market for these goods is not moral. This creates grounds sufficient for regulating, and possibly prohibiting, these goods. To help determine whether it is then necessary to regulate the goods, I propose a supplementary economic analysis to ascertain why an assumption regarding a particular preference is being violated.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 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".