Rankings, Reputation and Status: Novel Market Dynamics
Bibliographic record
Abstract
Third-party evaluations -- such as rankings, awards, and aggregated consumer reviews -- have grown in influence over the last decades. This change has opened several important avenues of inquiry; yet, our understanding of how third-party evaluators impact markets remains nascent. One major knowledge gap involves understanding the complications that arise as evaluators proliferate and organizations increasingly receive multiple evaluations from different evaluators. In this symposium, we compile four different empirical studies using different contexts and methods to shed light on the complexity created by heterogeneous external evaluations. Through this symposium, we hope to build awareness of the unique dynamics of markets with multiple evaluators, as well as the unexpected consequences of third-party evaluations. Ranker Reactivity: An Investigation of Ranking Spillover Effects across Multiple Intermediaries Author: W Chad Carlos; Brigham Young U. Author: Yasir Dewan; HEC Paris Author: Ben William Lewis; Brigham Young U. Author: Brian Philip Reschke; Brigham Young U. Author: Isaac St. Clair; Brigham Young U. Paths of Glory: How Centrality in Attention Networks Affects Status Evaluations Author: Matteo Prato; ESADE Business School Author: Raquel Pruna; ESADE Business School Author: Anne Bowers; U. of Toronto The Experts and The Crowd: How Rankings Affect Consumer Ratings Author: Clara Depalma; Department of Management and Technology, Bocconi U. Author: Giada Di Stefano; Bocconi U. Author: Saverio Dave Favaron; SKEMA Business School CSR at the Margins: Firms’ Responses to Marginal Inclusion on the Vault Law 100 Author: Wooseok Jung; HEC Paris Author: Amanda Sharkey; W. P. Carey School of Business, Arizona State U.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".