The Legitimacy of Power in Status-Differentiated Groups
Bibliographic record
Abstract
Abstract Purpose To use a behavioral measure of legitimacy to study how differences in negotiating style and status affect the legitimacy of persons in high-power network positions. Predictions include (1) that powerful network actors who negotiate using a pro-group style will maintain legitimacy better than will those who negotiate selfishly and (2) those higher in status will be granted more legitimacy both before and after exchange than powerful actors lower in status. Method An experimental study in which participants were connected in networks to powerful partners who were portrayed as consistently high or low on several status characteristics. Both before and after exchange, participants evaluated partners on a number of dimensions and made decisions on whether to vote to join a coalition to take the partner's power away, a direct behavioral indicator of legitimacy. Findings High-power partners lost legitimacy over the course of exchange irrespective of whether they negotiated in pro-group or selfish ways, and irrespective of whether they were high or low in status. This effect was pronounced for partners who negotiated selfishly. Although partner status predicted legitimacy prior to exchange, legitimacy evaluations after exchange appeared entirely driven by the partner's negotiating style (how the power was used) and not by status. Research Implications The project introduces a new behavioral measure of legitimacy that correlated highly with self-report items and should be of value in future research. The study also indicates promising directions for future research that might disentangle effects of power and status on legitimacy, along with adjudicating among explanations for why this study did not find status effects on legitimacy.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".