The Role of Third-Party Rankings in Status Dynamics: How Does the Stability of Rankings Induce Status Changes?
Bibliographic record
Abstract
Most explanations of status dynamics rely on market actor behavior or affiliation to other actors as the primary drivers of change. Yet status is increasingly mediated by third-party intermediaries, which impart status through their ordering of actors. Prior literature suggests that these rankers can affect status orders via changes in the underlying ranking methodology but offers little insight as to whether such changes reflect existing field beliefs or are self-interested. We advance a theory of ranker self-interest, whereby rankers adopt specific behavior to maintain audience attention and increase their chance for survival. We hypothesize that, by threatening audience attention, temporal stability in rankings (an endogenous property of many status systems) induces rankers to self-generate changes in the ranking. We examine the role of stability of rankings in promoting structural changes by rankers using Institutional Investor magazine’s All-America Research Team (all-stars), a widely studied and eminently impactful ranking of equity analysts.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".