In search of a transnational capitalist class: Alternative methods for comparing director interlocks within and between nations and regions
Bibliographic record
Abstract
Theorists of globalization have hypothesized the emergence of a transnational capitalist class that is becoming increasingly integrated across national borders. One method of evaluating this hypothesis has been to apply network analysis to study the frequency and pattern of transnational ties within global interlocking directorates. The results of such studies are mixed, both as regards the extent of transnational interlocking and its regional distribution. In an effort to resolve this ambiguity and advance the state of research in this area we undertake two main tasks. First, we submit the prevailing methodology used in such studies to a critical evaluation in which we identify and address some of its theoretical and methodological limitations. Second, we introduce and illustrate three alternative methods for assessing the extent and pattern of global interlocking directorates. Each method conceptualizes transnational interlocking in a slightly different manner and brings different aspects of the process into focus. Despite these differences, all four methods point to the conclusion that a transnational capitalist class is very far from being realized on a global scale. On the other hand, the combined evidence is much stronger and relatively consistent for the emergence of a more circumscribed transnational capitalist class, centered in the North Atlantic region, which has made significant strides in transcending national divisions within and between Europe and North America.
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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 | 0.051 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".