"Stamps of Power and Conflict: Imprinting and Influence in the U.S. Senate, 1973-2009"
Bibliographic record
Abstract
Structural power is often assumed to lead to influence. Yet people vary in their ability to convert power into influence, and the experience of power can itself sow the seeds for the loss of influence. We bring a temporal, historical perspective to account for these disparities and apparent contradictions. We theorize that the gain or loss of power produces corresponding shifts in influence; however, these effects are contingent upon people’s experiences with power and conflict at the time of organizational entry. Individuals who enter an organization wielding considerable structural power can acquire enduring cognitive rigidities--a stamp of power--that subsequently make them less influential, while those who initially experience conflict can derive lasting learning benefits--a stamp of conflict--that later make them more influential. We evaluate and find support for these propositions in analyses of the U.S. Senate from 1973 to 2009: (1) senators became more influential when their party moved into the majority and when they became committee chairs; (2) entering the Senate as a member of the majority party dampened senators’ subsequent ability to exert influence; and (3) initial assignment to politically divided committees enhanced senators’ later influence. We discuss implications for research on power, conflict, and imprinting.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".