Implicit and Explicit Occupational Gender Stereotypes

By M. White, Gwendolen B. White. 2006

225 citations recorded by the source corpus.

Subfields: Gender Studies, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f8d7cbb5a5197243f007c0b382279427c59915f9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Gender Studies

1. H. Lips
2. Sheila R. van Berkel
3. M. Bakermans‐Kranenburg
4. E. T. Hallers-Haalboom
5. M. Groeneveld
6. A. Smeding
7. J. Endendijk
8. J. Mesman

In Safety Research

1. H. Lips
2. M. Bakermans‐Kranenburg
3. Jose Miguel Such
4. Natalia Criado
5. A. Smeding
6. Xavier Ferrer Aran

In Sociology and Political Science

1. H. Lips
2. Sheila R. van Berkel
3. M. Bakermans‐Kranenburg
4. E. T. Hallers-Haalboom
5. Jose Miguel Such
6. Natalia Criado
7. M. Groeneveld
8. A. Smeding

Citations in the corpus

• Gender Stereotypes in the Family Context: Mothers, Fathers, and Siblings (2013, methodology): “…are better predictors of behavior than explicit self-reported stereotypes (Nosek et al., 2002a, 2002b; Nosek et al., 2005; Rudman, 2004), because explicit reports may be biased by social desirability and a lack of awareness of own stereotypes (Kunda & Spencer, 2003, White & White, 2006).”
• The Gender Pay Gap: Challenging the Rationalizations. Perceived Equity, Discrimination, and the Limits of Human Capital Models (2012, background): “Rather, there is evidence for the continued existence of both explicit and implicit occupational gender stereotypes among university students (White and White 2006).”
• Women in Science, Technology, Engineering, and Mathematics (STEM): An Investigation of Their Implicit Gender Stereotypes and Stereotypes’ Connectedness to Math Performance (2012, background): “…gender stereotypes implicitly, as has been done in German and U.S. research (Nosek et al. 2002; Steffens and Jelenec 2011; Stout et al. 2011; White and White 2006), because explicit measures are susceptible to motivational biases and introspective limitations (Greenwald and Banaji 1995;…”
• Attesting Biases and Discrimination using Language Semantics (2019, background): “ader share one key (and male and helper share the other). This speed difference indicates the presence of an implicit gender stereotype that associates male with leadership more frequently than female [33]. IAT has been also proved useful to measure racial and ethnic biases [19], religious implicit b”