Sticky ties among rivals for entrepreneurial leadership
Bibliographic record
Abstract
Purpose We explore the social structure of an autocratic, entrepreneurial organization, with a focus on the enduring rivalry between its formal leader and a key employee. Design/methodology/approach This is a re-study of Team Entrepreneurship (Stewart, 1989), with different eyes and methods, using ethnographic data for social network analysis (SNA). Findings With an analysis of structural equivalence, multiplexity, Simmelian triples and intermediation, this study shows how apparently conflicting social networks—collective entrepreneurship, antagonism and analytical management—can co-exist and that the main rivalrous tie is persistent or “sticky”. Research limitations/implications Due to deep fieldwork access and a site of managerial rivalry, the findings would have damaged careers, without a wait of many years to make the findings public. The specific context also limits external validity. The strengths of this paper are methodological and conceptual. Originality/value This is a rare observational study of entrepreneurial leadership and a rare study in an organization that uses ethnographic data as inputs for SNA matrixes. It shows important distinctions between inferences from observations compared with SNA.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".