Bibliographic record
Abstract
We develop a classification scheme of systems of exchange using concepts from network analysis, economics, and cultural sociology. This classification illustrates that the “free market ” is but one possible type of economy and that other types are not best understood as imperfections. This classification helps to distinguish analytically among qualitatively different types of exchange arrangements and suggests bases from which to develop theories about the organization of markets and exchange systems of various types. Thirty years ago, organization theorists fo-cused largely on the internal organization of firms and other bureaucratic structures. In re-cent years, however, they have turned increas-ingly toward trying to understand the economic context within which firms operate. Prompted especially by the development of networked markets in Asia, organization scholars have rec-ognized that market organization, not only firm organization, is a crucial factor in explaining the activities and character of firms and other economic actors. When organization analysts turned toward the examination of markets, the prevailing con-ceptualization of a market came from econom-ics. Being a parsimonious construct, the eco-nomic idea of a market failed the needs of many empirically oriented organization analysts, par-ticularly those rooted in anthropological and so-ciological traditions. Subsequently, in a flurry of writings, scholars promoted alternative ways of conceptualizing market organization. Two sets of ideas about the nature of markets emerged. One conceptualizes markets as structures of so-cial relations and focuses on the organization of market roles into status hierarchies and net-
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".