Rapid Rise or Steady Climb: The Effect of Status Trajectories on Screen Performers’ Next Roles
Bibliographic record
Abstract
It is well-established that winning prestigious awards enhances the status of social actors—be they individuals, groups, or organizations. But how does award recognition shape actors' next moves? Prior research offers competing insights: some studies suggest that high-status actors select conventional pursuits to protect their heightened position, while others that high status affords actors more latitude to deviate from norms. However, what drives these opposite responses is commonly explained by static factors, assuming that actors’ willingness or ability to defy convention depends on the stability of the status hierarchy, or individual characteristics. Taking a more dynamic perspective, I contend that what actors do next is also contingent on the trajectories they took before winning their award. I test my argument by examining the acting roles that Hollywood performers took over time. I found that performers who won a prestigious Oscar were more likely to focus on conventional high-status roles. However, actors who had been previously nominated prior to winning were more likely to pursue unconventional roles of lower status. Metaphorically, awards produced different responses depending on whether screen performers rapidly rose or steadily climbed their way to the top. This study adds an important dynamic perspective to our understanding of the role of awards and status in markets.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".