Frontiers of Hierarchy Research: Status, Power, and Inequality
Bibliographic record
Abstract
Hierarchy is an essential component of social life, which emerges spontaneously and organize the social dynamics (Durkheim, 1960; Magee & Galinsky, 2008). Two of the most widely studied and fundamental hierarchical dimensions are status and power (Blader & Chen, 2012; Fiske, 2010; Kemper, 2006; Weber, 1964). Status is defined as the prestige, respect, and esteem that an individual or a group has in the eyes of others (Anderson & Kilduff, 2009; Magee & Galinsky, 2008) and power is defined as individuals’ asymmetric control over valuable resources (Blau, 1964; Greer et al., 2017; Magee & Galinsky, 2008). Previous studies on status and power have shown their impacts on a variety of important outcomes, such as social resources (Lin, 1999), emotions (Kemper, 2006), learning (Bunderson & Reagans, 2011), and goal seeking (Guinote, 2017). This symposium aims to contribute to continuing the discussion of social hierarchy in impacting social life with novel perspectives on both traditional concepts such as gender differences and competition/cooperation, and understudied yet important phenomena such as individual exploration and discrimination recognition. We believe that our symposium will help generate new perspectives and important questions regarding social hierarchy and its relationships with various constructs. We hope that it will facilitate sophisticated theorizing and rigorous empirical research in this line of research.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".