The institutions of the market : organizations, social systems, and governance
Bibliographic record
Abstract
Introduction: The Institutions of the Market PART I: MARKET AGENTS: KNOWLEDGE AND LEARNING IN ORGANISATIONS The Emergence of the Idea of Institutions as Repositories of Knowledge Dynamic Capability as a Source of Change Rules, Routines, and Learning in Organizations Problemistic Search and (Inter-) Organizational Learning Change in Institutional Fields PART II: MARKET PROCESS: RULES, NORMS AND THE SOCIAL SYSTEM OF MARKET COMPETITION Myths of the Market On the Social Structure of Markets: A Review and Assessment in the Perspective of the New Institutional Economics Institutional Entrepreneurship and the Structuring of Organizations and Markets Organizational Ecology as a Theory of Competition Different Paths of Industry Evolution: Timing of Entry, Legitimation and Competition Spillovers Across Countries PART III: MARKET GOVERNANCE: REGULATION, COORDINATION AND PUBLIC POLICY What Makes an Economy Productive and Progressive? What are the Needed Institutions? Institutional Evolution and the Political Economy of Governance Explaining Economic Change: The Relations of Institutions, Politics, and Culture Polanyian, Regulationist, and Autopoieticist Reflections on States and Markets and their Implications for Knowledge and the Knowledge-Based Economy Pierre Bourdieu - Analyst of Change? The View from Regulation Theory
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".