ГЛАВА 3 «НА КАБЛУКАХ И ЗАДОМ НАПЕРЕД»
1 For meta-analysis, see (Voyer, Voyer, & Bryden, 1995).
2 (Moore & Johnson, 2008; Quinn & Liben, 2008). It’s worth noting that the early appearance of this difference does not necessarily mean that experiential factors could not be responsible. For example, male babies could be given more gross stimulation that stimulates visuospatial skills. Interestingly, one study found that boys and girls from a low socioeconomic background underperformed equally on a visuospatial task, whereas more-privileged boys outperformed their female counterparts. This points towards the importance of experiential factors in male advantage (Levine et al., 2005). Moreover, an early advantage for males doesn’t mean that this must inevitably persist. In other cognitive domains, gender differences are transient.
3 Needless to say, this is a complex issue. As Nora Newcombe recently summarised it, not only do men, on average, outperform women on mental rotation tasks, particularly at the highest levels, spatial visualisation skills are relevant to success in fields such as physics, mathematics, computer science and engineering. However, as she also notes, there are difficulties with the argument that these genuine sex differences are biologically caused and immutable. With regard to the first point – biological causation – she notes that hypotheses attempting to account for biological mechanisms have not been successful. (Two of these, hormonal accounts and sex differences in lateralisation, are discussed in the second part of the book. The other ideas – an X-linked recessive gene for spatial ability and males’ later puberty – have not been supported by the evidence.) Newcombe also notes that, despite superficial plausibility, evolutionary explanations entail numerous untested assumptions. One further important point raised by Newcombe is whether extra increments in mental rotation ability are important, beyond some high threshold. (As Amanda Schaffer dryly put it in Slate, ‘when it comes to the diverse precincts of high-level science, spatial reasoning only gets you so far. Rock-star academics don’t necessarily spend their days turning geometric figures around in their minds.’) Newcombe points out that ‘[t]hinking creatively, explaining one’s data, or inspiring a research team may be pretty important as well!’ (Newcombe, 2007), p. 75. A recent, very comprehensive review of ‘sociocultural and biological considerations’ with respect to women’s underrepresentation in science concluded that the ‘process needed to establish male advantage in STEM fields as a function of superior spatial ability (possibly because of its role in advanced mathematics) is littered with loopholes. Nothing close to a tightly reasoned and supported argument currently exists.’ (Ceci, Williams, & Barnett, 2009), p. 250.
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4 Reviewed, for example, by (Newcombe, 2007). Recent studies have also found that playing computer games improves mental rotation ability, and in women more so than in men (Cherney, 2008; Feng, Spence, & Pratt, 2007).
5 (Sharps, Price, & Williams, 1994). Task instructions quoted from pp. 424 and 425. Men in the masculine condition outperformed men and women in all other groups. See also (Moè & Pazzaglia, 2006).
6 (McGlone & Aronson, 2006).
7 (Hausmann et al., 2009).
8 (Moè, 2009).
9 M. B. Ritter, More than gold in California 1849–1933 (Berkeley, CA: The Professional Press, 1933), p. 161. Quoted in (Morantz-Sanchez, 1985), p. 118.
10 C. M. Steele, S. J. Spencer, & J. Aronson, ‘Contending with group image: The psychology of stereotype and social identity threat’. In M. P. Zanna (ed.), Advances in experimental social psychology, vol. 34 (San Diego: Elsevier, 2002), p. 385. Quoted in (Shapiro & Neuberg, 2007), p. 109.
11 Readers interested in reading more about stereotype threat are strongly recommended to visit the Web site http://reducingstereotypethreat.org, authored by social psychologists Steven Stroessner and Catherine Good, which provides detailed and comprehensive coverage of the academic literature.
12 (Good, Aronson, & Harder, 2008).
13 For example (Marx & Stapel, 2006b; Marx, Stapel, & Muller, 2005; Thoman et al., 2008).
14 (Good, Aronson, & Harder, 2008), p. 25.
15 (Walton & Spencer, 2009), p. 1133. Although they note that their samples may not be representative of the general population, their effect sizes suggest that the SAT Maths may underestimate women’s abilities by about 20 points (compared with a gender gap of 34 points). For African and Hispanic Americans, SAT Reading tests may underestimate ability by about 40 points.
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16 For example (Adams et al., 2006; Danaher & Crandall, 2008; Davies et al., 2002; Inzlicht & Ben-Zeev, 2000; Logel et al., 2009).
17 See (Nguyen & Ryan, 2008).
18 (Marx, Stapel, & Muller, 2005).
19 For example (Cadinu et al., 2003; Stangor, Carr, & Kiang, 1998) and (Marx & Stapel, 2006a), p. 244. As David Marx has argued, and his work has been demonstrating, priming a self-relevant stereotype has effects different from, and greater than, standard stereotype priming effects.
20 (Cadinu et al., 2005), p. 574.
21 (Logel et al., 2008). See also (Davies et al., 2002) who found that gender stereotypes were activated in women who saw gender-stereotyped advertisements, compared with controls, and that this activation predicted maths underperformance.
22 (Beilock, Rydell, & McConnell, 2007; Schmader & Johns, 2003). For review see (Schmader, Johns, & Forbes, 2008).
23 (Johns, Inzlicht, & Schmader, 2008).
24 For example (Aronson et al., 1999; Croizet et al., 2004).
25 Presenting the test as gender-neutral (i.e., males and females score equally) enhances women’s performance (for example [Spencer, Steele, & Quinn, 1999]), and does not have the same detrimental effect on working memory (for example [Johns, Inzlicht, & Schmader, 2008]).
26 (Seibt & Förster, 2004).
27 (Gladwell, 2008), pp. 87, 87, and 88, respectively.
28 See (Nguyen & Ryan, 2008) who concluded from their meta-analysis that low maths-identified women are the least affected by stereotype threat. Interestingly, they found that moderately identified women are the most affected (more so than high-identified females), although they note that there is some inconsistency in how ‘identification’ is defined and operationalised.
29 For instance (Beilock, Rydell, & McConnell, 2007) found that stereotype threat most affects maths problems that rely more heavily on working-memory resources.
30 These numbers, from the National Science Foundation, are cited in (Ceci, Williams, & Barnett, 2009).
31 (Inzlicht & Ben-Zeev, 2000).
32 (Schmader, Johns, & Barquissau, 2004).
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33 (Kiefer & Sekaquaptewa, 2007).
34 See (Blanton, Crocker, & Miller, 2000; Marx, Stapel, & Muller, 2005). For effect of ‘closeness’ of the model, see (Marx et al., unpublished manuscript), who found that women exposed to a highly maths-competent, socially ‘close’, female role model performed better on a maths test than women exposed to a socially ‘distant’, but equally competent, female role model. (Lockwood, 2006) found that women in particular benefit by having an inspiring female role model. In general, research into social comparison processes finds that, among other factors, our self-evaluations and behaviour are more likely to assimilate to another person to the extent that we feel psychologically similar to them. Otherwise the standard set by the other person becomes a contrast against which our own self-evaluation and behaviour reacts. See, for example (Mussweiler, Rüter, & Epstude, 2004).
35 (Marx & Roman, 2002; McIntyre et al., 2005; McIntyre, Paulson, & Lord, 2003).
36 (Josephs et al., 2003; Newman, Sellers, & Josephs, 2005).
37 See (Rogers, 1999), pp. 75–85. It’s also worth noting that although some have argued that the relationship between testosterone levels and competition is different in women and men, there are currently too few studies available with women to draw a fair comparison. See (van Anders & Watson, 2006), pp. 215–220.
38 (Sherwin, 1988).
39 (Rogers, 1999), p. 83.
40 (Josephs et al., 2003), p. 162.
41 (Huguet & Régner, 2007; Neuville & Croizet, 2007). Also (Ambady et al., 2001) found stereotype threat effects in lower-elementary and middle school girls, although unexpectedly upper-elementary girls did better when gender identity was salient.
42 (Nosek et al., 2009), p. 10597. These relationships held even controlling for a general indicator of social gender inequality.
ГЛАВА 4 «МНЕ ЗДЕСЬ НЕ МЕСТО»
1 (Hines, 2004), p. vii.
2 (Haslanger, 2008), p. 211.
3 Quoted in (McCrum, 2008) p. 22.
4 (Mullarkey, 2004), pp. 369 and 370, respectively.
5 (Mullarkey, 2004), pp. 373 and 374.
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6 (Pinker, 2008), p. 5.
7 (Steele, 1997), p. 618.
8 (Murphy, Steele, & Gross, 2007).
9 (Davies et al., 2002).
10 (Davies, Spencer, & Steele, 2005).
11 (Gupta & Bhawe, 2007), p. 74.
12 A point made by (Cheryan et al., 2009).
13 (Cheryan et al., 2009).
14 I. J. Seligsohn, Your Career in Computer Programming (New York: Simon & Schuster, 1967), cited in (Gürer, 2002a), p. 176.
15 (Gürer, 2002b), p. 120.
16 Sapna Cheryan, personal communication, November 25, 2009.
17 (Cheryan et al., 2009), p. 1058.
18 (Spelke & Grace, 2006), p. 726.
19 The criteria were changed to downplay prior programming ability – which was shown not to be a predictor of success in the CS major, and instead focus on ‘indicators of future visionaries and leaders in computer science.’ (Blum & Frieze, 2005), p. 117. The study referred to was conducted by Jane Margolis and Allen Fisher, reported in Unlocking the Clubhouse (Cambridge, MA: MIT Press, 2002).
20 (Blum & Frieze, 2005), quotations from pp. 113 and 114.
21 (Good, Rattan, & Dweck, unpublished).
22 (Haslanger, 2008), p. 212.
23 (Correll, 2001), p. 1724.
24 (Correll, 2004), p. 102.
25 (Pronin, Steele, & Ross, 2004).
26 The article was C. P. Benbow and J. C. Stanley, ‘Sex differences in mathematical ability: fact or artifact?’ Science 210 (1980), pp. 262–1264.
27 Quoted in (Pronin, Steele, & Ross, 2004), p. 159.
28 (Hewlett, Servon et al., 2008), p. 11.
29 (Hewlett, Luce, & Servon, 2008), p. 114.
30 (Hewlett, Servon et al., 2008), quotations from pp. 11 and 12.
31 For instance, from their comprehensive review of possible biological and social factors contributing to female underrepresentation in science, Stephen Ceci and colleagues conclude that the evidence for the role of biological factors is ‘contradictory and inconclusive.’ They suggest that the evidence points most strongly to the role of women’s preferences – which they note could either be seen as free choices or constrained ‘choices’ – with a secondary factor being poorer performance on gatekeeper tests, which they regard as being more likely due to sociocultural than biological factors (Ceci, Williams, & Barnett, 2009), p. 218.
ГЛАВА 5 «СТЕКЛЯННОЕ РАБОЧЕЕ ПРОСТРАНСТВО»
1 (Fara, 2005). See pp. 93–96.
2 (Barres, 2006), p. 134.
3 (Schilt, 2006), p. 476.
4 See also data and arguments provided by (Valian, 1998).
5 (Steinpreis, Anders, & Ritzke, 1999). Estimated from figure 5, p. 520.
6 (Steinpreis, Anders, & Ritzke, 1999), p. 523.
7 (Davison & Burke, 2000). However, sex discrimination was greater when less job-relevant information was available.
8 (Heilman, 2001), p. 659.
9 (Biernat & Kobrynowicz, 1997).
10 Interestingly, when evaluations were made on vague, subjective scales (very poorly to very well, or very unlikely to very likely), Katherine was preferred for the chief of staff position, while Kenneth was favoured as a secretary. However, the researchers suggested that this was because Katherine was seen as being a good candidate for the masculine job for a woman, while Kenneth was perceived as an impressive potential secretary for a man. When more objective scales were used that forced the raters to put numbers and percentiles to their evaluations, the pattern reversed.
11 (Correll, Benard, & Paik, 2007). Participants were undergraduates, told that their input would be used along with other information in real hiring decisions.
12 See, for example (Crosby, Williams, & Biernat, 2004) and other articles in the same issue.
13 (Bledsoe, 1856), pp. 224 and 225.
14 (Rudman & Kilianski, 2000).
15 See, for example (Rudman, 1998; Rudman & Glick, 1999, 2001). For summary of research suggesting that warmth and competence are fundamental dimensions of social perception, see (Fiske, Cuddy, & Glick, 2007).
16 A phrase coined by Janet Holmes, author of Gendered Talk, cited by (Cameron, 2007), p. 141.
17 M. Dowd, ‘Who’s hormonal? Hillary or Dick?’ New York Times, February 8, 2006, p. A21, quoted by study authors (Brescoll & Uhlmann, 2008), p. 268.
18 (Cuddy, Fiske, & Glick, 2004). Interestingly, in this study gender per se was not a factor for discrimination, although it’s possible that this was because of the phenomenon described in note 10.
19 (Rudman et al., manuscript submitted for publication).
20 (Norton, Vandello, & Darley, 2004).
21 (Uhlmann & Cohen, 2005), p. 479.
22 (Uhlmann & Cohen, 2005), p. 478, references removed.
23 (Phelan, Moss-Racusin, & Rudman, 2008), p. 408.
24 Quoted in (Monastersky, 2005), para. 42.
25 (Bolino & Turnley, 2003; Bowles, Babcock, & Lai, 2007; Butler & Geis, 1990; Heilman & Chen, 2005; Heilman et al., 2004; Sinclair & Kunda, 2000).
26 (Heilman, 2001), p. 670.
27 (Cameron, 2007), pp. 134 and 135.
28 (Ryan et al., 2007), p. 270.
29 See (Ashby, Ryan, & Haslam, 2007; Haslam & Ryan, 2008). Other data forthcoming, summarised in (Ryan et al., 2007), pp. 270 and 271.
30 (Uhlmann & Cohen, 2005).
31 (Williams, 1992), p. 256.
32 (Wingfield, 2009).
33 (Gorman & Kmec, 2007), p. 839.
34 Quoted in (Allen, 2009), para. 7.
35 (Hersch, 2006), p. 352.
36 (Liben, Bigler, & Krogh, 2001).
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