The Effect of Gender Stereotypes on Perceived Decision Making Abilities
Bibliographic record
Abstract
The purpose of this study was to explore and study the effects of both positive and negative stereotypes on the perceived decision making abilities of those exposed to them. Past research has indicated that prejudice towards women exists more prevalently and more intensely in society than it does for males. Accordingly, we predicted that women would respond more negatively to such stereotypes than men would when presented with them. We then measured this by asking participants to judge their perceived agency in hypothetical situations that required decision making. We tested this with both positive and negative stereotypes, and also with a control. Our findings did produce significant results showing a main effect of participant gender (female) causing them to rate themselves more negatively then males on average. These results are important because they show that women respond negatively to these pervasive and flawed stereotypes attributed to their gender which could negatively affect their ability to make efficient decisions in everyday life. the effect of Gender stereotypes on perceived decision MAkinG Abilities This study was conducted to find out if stereotypes have an effect on peoples’ perceptions of their own ability to make decisions. This relies on these stereotypes existing pervasively in our society, and also that people (specifically women) are aware of them on at least some level of consciousness. Past research has studied and shown evidence that these stereotypes do in fact pervade throughout society: “Also, social scientists are in general agreement that women face discrimination in many occupations...”1 ...and are also perceived, both consciously and subconsciously, by both women and men: “Moreover, women themselves, although not necessarily believing themselves personally deprived, do perceive that women as a group are unjustly treated.”2,3 “...therefore, anti-female bias, often functioning out of people’s conscious awareness...”4 The fact that these stereotypes exist in society, enough to warrant recognition by many organizations and interest groups raises the question of how they might affect those they are directed towards. From this past research and the questions that followed, we extrapolated that these stereotypes could be having profound effects on both the men and women that they targeted. Specifically, we wanted to know how they may affect the way a person regards their ability to make decisions when presented with hypothetical scenarios. Thusly, we hypothesized that gender stereotypes could ultimately affect a person’s perceived decision making abilities concerning future events We expected to find that when presented with positive stereotypes, participants would perceive themselves as 1 Eagly, A. H., & Mladinic, A. (1994). Are people prejudiced against women? Some answers from research on attitudes, gender stereotypes, and judgments of competence. European Review of Social Psychology, 5, 1-35. 2 Crosby, F. (1982). Relative Deprivation and Working Women. New York: Oxford University Press. 3 Major, B. (1989). Gender differences in comparisons and entitlement: Implications for comparable worth. Journal of Social Issues, 45(4), 99-115. 4 Banaji, M. R., & Greenwald, A. G. (1994). Implicit stereotyping and prejudice. In M.P. Zanna & J. M. Olson (Eds). The Psychology of Prejudice: The Ontario symposium (Vol. 7, pp. 55-76). Hillsdale, NJ Erlbaum.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".