The Effects of Experimenter Gender on State Social Physique Anxiety and Strength in a Testing Environment
Bibliographic record
Abstract
Social influences can impact self-presentational concerns such as social physique anxiety (SPA), concerns over one's body being evaluated by others. In addition, social influences may also impact performance on a physical test. In a physical testing environment, one social factor that influences SPA and which may also influence the outcomes of a physical test is experimenter gender. The present study examined the influence of experimenter gender on SPA and actual muscle strength in an experimental testing environment. Male (n = 50) and female (n = 50) university students were randomly assigned to either a male or female experimenter. Before strength testing, state SPA (SPA-S) was assessed. Actual strength was represented by the score obtained during the maximum voluntary contraction (MVC) test. Two 2 × 2 (participant gender × gender of the experimenter) analyses of variance were conducted with SPA-S and strength as the dependent variables. For SPA-S, a significant main effect was found only for participant gender (F(1,95) = 14.08, p < 0.01, η² = 0.13), with women scoring significantly higher than men. For MVC, there was a significant effect for participant gender (F(1,96) = 48.08, p < 0.001, η² = 0.33), with men, as expected, having significantly higher strength values than women. Although the gender of the experimenter did not influence SPA-S or muscle strength, other forms of anxiety (e.g., fitness anxiety) may be relevant in this setting. Future research should also investigate other factors in the testing environment (e.g., type of task) that may be more influential on psychological or performance outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".