Social Structure and Exchange: Self-confirming Dynamics in Hollywood
Bibliographic record
Abstract
This study uses data on the U.S. film industry from 1982 to 2001 to analyze the effects on box office performance of prior relationships between film producers and distributors. In contrast to prior studies, which have appeared to find performance benefits to both buyers and sellers when exchange occurs embedded within existing social relations, we propose that the apparent mutual advantages of embedded exchange can also emerge from endogenous behavior that benefits one party at the expense of the other: actors offer better terms of trade and allocate more resources to transactions embedded within existing social relations, thereby contributing to the ostensible advantages of such exchange patterns. Findings show that not only do distributors exhibit a preference for carrying films involving key personnel with whom they had prior exchange relations, but also they tend to favor these films when allocating scarce resources (opening dates and promotion effort). After controlling for the effects of these decisions, films with deeper prior relations to the distributor perform worse at the box office. The results suggest that, rather than benefiting from repeated exchange, distributors overallocate scarce resources to these prior exchange partners, enacting a self-confirming dynamic.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".