Bibliographic record
Abstract
When feeling powerful humans and other animals display expansive postures, but can posing in expansive and powerful postures also generate empowerment? Researchers have studied the “power posing effect” the concept that powerful expansive postures generate empowerment, and found conflicting evidence. Some evidence of power posing’s impact shows increased hormones and a variety of behaviors indicating greater confidence. Yet still others have found no effect on hormones or behaviors, and suggest the impact of power posing is overstated. The goal of this project was to replicate and extend previous knowledge and contribute to the debate as to the efficacy of power posing, specifically examining the impact on participants’ self-reported social problem-solving efficacy, self-esteem, and optimism. 119 participants were randomly assigned to one of three conditions: high power pose, low power pose, or a control group with a puzzle solving task, and asked to complete self-report measures of optimism, self-esteem, and problem-solving self-efficacy. Current findings suggest expansive posture demonstrates no measurable impact on psychological attitudes, and contributes to recent literature contradicting the power posing effect. Research and practical implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".