Interlocking directorates and corporate networks
Bibliographic record
Abstract
This chapter provides an introduction to the literature on interlocking directorates and corporate networks. It first traces the historical roots of the field back to the early 20th century, when researchers on both sides of the Atlantic started expressing concern about the threat to democratic process posed by the emergent corporate form, the potential for collusion allowed by the growing practice of interlocking directorate, and the general concentration of power in the hands of large firms and banks. It then outlines the major theoretical approaches employed, that focus on the corporate network as a set of both interorganizational and interindividual relationships. Third, it summarizes the main findings on the cohesiveness of the corporate community, the hegemonic position of banks, and historical changes and longitudinal dynamics of the network. Finally, it discusses most recent debates on the globalization, the emergence of a European corporate network, and the decline and recomposition of the corporate community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".