Market and Society: How do they relate, and contribute to welfare?
Bibliographic record
Abstract
This paper discusses how markets and society relate to each other. We present and discuss three views: markets as separate, markets as embedded, and markets as impure. One’s stance on the contribution of markets to welfare hinges on the conceptualization of market and other spheres in society. If, for instance, one perceives of the economy (the economic domain) as an all-encompassing sphere or as a sphere totally separate from others, then one would believe markets necessarily contribute to welfare. Markets are presumed to be ubiquitous in mainstream economics; the orthodox view is that of the ‘market as separate’. Indeed, Frank Hahn notably conceded that neoclassical economics does not describe markets, but ‘conjures’ them up. Mainstream conceptions of the market are functionalist – in the appropriate conditions the market is an efficiency conduit, and hence wealth and welfare generating. Creating these appropriate conditions then drives policy, such as the provision of health care, and tends to produce a one size fits all approach. This paper argues that this is an overly restrictive conceptualization of markets, and is an inadequate basis for conceptualizing the potential effects of markets. Conceptualizing the market as impure and embedded must be added. We contribute to this discussion by developing the concepts of ‘boundaries’ separating spheres. Such an approach broadens the notion of welfare and well-being beyond the monetized parameters of economic orthodoxy.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.020 | 0.036 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".