Advantages and Disadvantages of Brokers in Alliances: A Two-Staged Multilevel Model
Bibliographic record
Abstract
Most previous research highlights the benefits of structural hole positions, but studies have surfaced conflicting evidence. We argue that structural holes are characterized by a collaboration paradox: organizations which span more structural holes are likely to have more opportunities for collaboration, but are less capable of exploiting those opportunities to attain superior performance in alliances. Brokers’ capacity to appropriate value is both their strength and their curse. Evidence collected from alliances among 2,694 movie production companies in the 2009-1994 periods demonstrates that broker organizations with more structural holes are more likely to form alliances; however alliances among broker organizations tend to be less successful. We found that brokers have an opportunity to be successful and break their curse only if they work in centralized alliances where they are a sole broker collaborating with non-brokers. However, non-brokers are less likely to ally with brokers. Therefore, paradoxically, brokers have more opportunities to form alliances that create less value, while having fewer opportunities to form alliances that create more value.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".