Interpersonal Processes of Power Dynamics
Bibliographic record
Abstract
Power is an interpersonal phenomenon. Because high-quality relationships are beneficial for the development of individuals and functioning of organizations, how to foster high-quality unequal-power relationships is an important topic to study. Drawing on the psychology literature on interpersonal relationships and power, more recent research has started to examine interpersonal processes in unequal-power relationships. This symposium presents four lines of research that use diverse methodologies and samples to explore various interpersonal processes between high-power and low-power individuals, such as prosocial behavior, expected trust, reactions to trusting behavior, self-disclosure, and controlling and autonomy-supporting behavior. This symposium aims to provide an opportunity for knowledge sharing and discussion among researchers who are interested in the interpersonal processes of power. The Allocation of Indirect Reciprocity within a Power Hierarchy Presenter: M. Ena Inesi; London Business School Presenter: Gabrielle Adams; U. of Virginia Presenter: Anurag Gupta; London Business School The Authenticity of Power Holders’ Trust Presenter: Marlon Mooijman; Jones Graduate School of Business, Rice U. Presenter: Maryam Kouchaki; Northwestern Kellogg School of Management Self-Disclosure in Unequal-Power Dyads Presenter: Yidan Yin; U. of California, San Diego Presenter: Pamela K. Smith; U. of California, San Diego Presenter: Christopher Oveis; U. of California, San Diego Aligning Social Hierarchy Motivation and Leader Behavior Presenter: Hun Whee Lee; The Ohio State U. Fisher College of Business Presenter: Nicholas Hays; Michigan State U. Presenter: Russell Eric Johnson; Eli Broad School of Business, Michigan State U.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".