The Structural Origins of Unearned Status: How Arbitrary Changes in Categories Affect Status Position and Market Impact
Bibliographic record
Abstract
Focusing on the categorical nature of many status orderings, we examine the relationship among status, actors’ quality, and market outcomes. As markets evolve, the number of categories that structure them can increase, creating opportunities for new actors to be bestowed status, or it can decrease, dethroning certain actors from their superior standing. In both cases, gains and losses of status may occur without changes in actors’ quality. Because audiences rely on status signals to infer the value of market actors, these exogenously generated status shifts can translate into changes in how audiences perceive actors, resulting in benefits for unearned status gains and costs for unearned status losses. We find support for our hypotheses in a sample of equity analysts at U.S. brokerage firms. Using data on the coveted Institutional Investor magazine All-Star award, we find that analysts whose status increases because of a category addition see corresponding increases in the stock market’s response to their earnings estimates, while those who lose status see corresponding reductions. Our results suggest that the greater weight accorded to high-status actors may be misguided if that status occurs for structural reasons such as category changes rather than because of an actor’s own quality.
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 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".