Delineating the corporate elite: Inquiring the boundaries and composition of interlocking directorate networks
Bibliographic record
Abstract
Abstract Corporate elite studies have for long investigated networks of interlocking directorates to test and corroborate key theoretical expectations regarding the cohesive organization of such an elite and their ability and willingness to act on behalf of general business interests. These studies typically collect data on a list of 50, 100, 200 or 500 corporations ranked by economic size, sometimes stratified in sectors. The sampling approach often follows previous studies in order to increase comparability. These relatively arbitrary sampling practices are problematic because they impact the empirical results and our therefore the conclusions drawn from it. Using a sample of 3251 Canada‐based corporations, we establish that indeed different sampling criteria – that is sample size, proportion of financial firms, inclusion of state‐owned enterprises and so on – significantly impacts network properties of corporate elite networks. We establish rather disturbing differences, especially for smaller sample sizes (<100). Subsequently, we develop alternative demarcation criteria of the corporate elite based on a k ‐core decomposition. We conclude by emphasizing that the sampling decisions in interlocking directorate studies should much more be carefully be thought through in future research on the topic, both in corporate elite studies and beyond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".