Power Affects Performance When the Pressure Is On
Bibliographic record
Abstract
The current research examines how power affects performance in pressure-filled contexts. We present low-power-threat and high-power-lift effects, whereby performance in high-stakes situations suffers or is enhanced depending on one's power; that is, the power inherent to a situational role can produce effects similar to stereotype threat and lift. Three negotiations experiments demonstrate that role-based power affects outcomes but only when the negotiation is diagnostic of ability and, therefore, pressure-filled. We link these outcomes conceptually to threat and lift effects by showing that (a) role power affects performance more strongly when the negotiation is diagnostic of ability and (b) underperformance disappears when the low-power negotiator has an opportunity to self-affirm. These results suggest that stereotype threat and lift effects may represent a more general phenomenon: When the stakes are raised high, relative power can act as either a toxic brew (stereotype/low-power threat) or a beneficial elixir (stereotype/high-power lift) for performance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".