Varieties of corporate networks: Network analysis and fsQCA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present research analyzes national corporate interlock networks and their causal conditions. The objective is two-fold: 1) to specify types of corporate networks, and 2) to pinpoint the causal configurations that give rise to each type of corporate network. First, corporate networks on basis of interlocking directorates are analyzed and compared using social network analysis to empirically derive a typology. The results show two types of corporate networks: cohesive corporate networks which are based on unification, centralization and strength ties; and dispersed corporate networks which are characterized by fragmentation, decentralization and single ties. Second, combinations of causal conditions that explain the emergence of each type of corporate networks are identified using fuzzy set qualitative comparative analysis (fsQCA). Finally, avenues of research on corporate interlock networks are suggested.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it