The Role of Third-Party Rankings in Status Dynamics: How Does the Stability of Rankings Induce Status Changes?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it