Section 1
Summaries of Convocation Sessions

Each session summary consists of an abstract of the panel and edited third-person transcripts of the speaker comments. The summaries present the views and opinions of the panelists and might not reflect the views of the committee or the National Academies. Slides presented by the panelists may be found on the convocation Web site, http://www7.nationalacademies.org/womeninacademe/Convocation.html.



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Section 1 Summaries of Convocation Sessions PANEL 1: Cognitive and Biological Contributions PANEL 2: Social Contributions PANEL 3: Organizational Structures PANEL 4: Implementing Policies Each session summary consists of an abstract of the panel and edited third-person transcripts of the speaker comments. The summaries present the views and opinions of the panelists and might not reflect the views of the committee or the National Academies. Slides presented by the panelists may be found on the convocation Web site, http://www7.nationalacademies.org/womeninacademe/ Convocation.html. 7

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9 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS PANEL 1 COGNITIVE AND BIOLOGICAL CONTRIBUTIONS Panel Summary Gender Differences and Similarities in Abilities Janet Hyde, Department of Psychology, University of Wisconsin at Madison Sexual Dimorphism in the Developing Brain Jay Giedd, National Institute of Mental Health, National Institutes of Health Environment-Genetic Interactions in the Adult Brain: Effects of Stress on Learning Bruce McEwen, The Rockefeller University Biopsychosocial Contributions to Cognitive Performance Diane F. Halpern, Berger Institute for Work, Family, and Children, Claremont McKenna College Selections from the Question and Answer Session Moderated by committee member Ana Marie Cauce

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10 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING PANEL SUMMARY The panel considered whether there are differences between males and females in brain development and in average performance on cognitive tasks and whether those differences account for the large discrepancies in male and female representation among academic scientists. Janet Hyde, of the University of Wisconsin-Madison, proposed the “novel concept of gender similarities” in cognitive abilities, noting that the mathematical, verbal and spatial skills involved in scientific work are all gender-stereotyped. Meta-analyses of 100 studies of math ability involving 3 million persons, includ- ing nine state assessments, show that the highly touted and widely reported gender differences in mathematical ability are in fact small or insignificant. Diane Halpern, of Claremont McKenna College, observed that men and women are in fact both similar and different and “what you see depends on where you look.” The differences or similarities found depend on which tests and measures are used. She also emphasized that nature and nurture form a “false dichotomy,” are not independent variables, and “do not just interact.” The factors are instead “inextricably intertwined” because experience alters the bio- logical underpinnings of behavior, and the resultant biology influences the types of experiences people have. Instead of the old two-part paradigm, she proposed a biopsychosocial conceptualization of the issue and the recognition that even small differences may have large effects over time because small effects accumulate into large ones. Jay Giedd, of the National Institute of Mental Health, presented data from magnetic resonance imaging (MRI) studies of brain structure and development during adolescence showing both gender differences in the trajectory of brain development and the strong and lifelong influence of experience on the brain. MRI studies show “gray boxes,” not individual neurons, and behavioral inter- pretations are therefore “speculative.” The sex hormones estrogen and testoster- one are present both in males and females, and play a role in brain development, although hormones are not sole factors driving sex differences in the brain. Male brains show more morphological variance than female brains, but observations are based on group averages and not individuals, and overall, the brains of males and females are more alike than different. Panelist Bruce McEwen, of Rockefeller University, presented evidence of complex sex differences in nonhuman brain response to stress and of the brain’s high adaptability and plasticity throughout the lifespan. Males and female humans differ in the processes and priorities they use in processing information. Genes, hormones, and experience exert different influences on human males and females, he concluded, but the cognitive differences between men and women appear to involve differing strategies of information processing rather than different “abilities.”

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11 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS GENDER DIFFERENCES AND SIMILARITIES IN ABILITIES Janet Hyde Department of Psychology, University of Wisconsin-Madison Janet Hyde’s presentation emphasized what she called “the novel concept of gen- der similarities” and focused on mathematical, verbal, and spatial abilities as ba- sic to science ability. Those abilities are “gender stereotyped,” with boys believed to excel on mathematics and spatial tests and girls on verbal measures.1 Hyde described the power of meta-analysis (see Box 1-1) and discussed a particularly large study of gender differences in mathematics performance that pooled the results of 100 studies that tested more than 3 million people and in- cluded a wide variety of data sources, such as assessments from nine states. Aver- aged over all samples of the general population, the d was equal to minus 0.05, “a tiny gender difference.” Another team of investigators obtained very similar re- sults using somewhat different meta-analytic techniques.2 Might there be an increasing gender gap in performance with age? Second, do the mathematics tests tap lower level math computation, or a deeper concep- tual understanding of mathematics and complex problem solving, which is needed to do science? Using meta-analytic methods to investigate these questions, Hyde found that girls are better than boys at computation by a small amount in elementary and middle school. For the deeper understanding of mathematical concepts, she found no gender difference at any age level. Finally, at the highest cognitive level, com- plex problem-solving, she found no gender difference in elementary school or middle school, but a small difference among high school and college students. Although that difference deserves attention, it is not large. The important point is that within-gender differences are enormous compared to between-gender differences. —Janet Hyde One explanation for the gender difference in problem-solving favoring high- school and college-age males is the difference in patterns of course taking. Girls have been less likely to take optional advanced mathematics classes in high school, although this gender gap has closed in the last five years. Girls now take calculus in high school at the same rate as boys. Nonetheless, they are less likely to take science courses in high school than boys, especially in chemistry and physics. This handicaps girls in pursuing science careers, and it also handicaps 1For more details, figures, and references, see Janet Hyde’s paper in Section 2. 2LV Hedges and A Nowell (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science 270:364-365.

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12 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING BOX 1-1 Meta-Analysis Hundreds of studies examine gender differences in performance. Rather than conduct an additional study, one can synthesize the existing studies to find an overall outcome. Meta-analysis refers simply to the application of quantitative or statis- tical methods to combine evidence from numerous studies. Meta-analysis can tell us, when we aggregate over all the available studies, whether there really is a gender difference in mathematical ability. It can tell us the direction of the difference: do males score higher on average or do females? And it can also tell us the magnitude of any gender difference. The d statistic, or effect size, is used to measure the gender differ- ence. To obtain d, the mean score of females is subtracted from the mean score of males in a particular study, and the result is divided by the pooled within-gender standard deviation. Essentially, d tells us how far apart the means for males and females are in standardized units. d can have positive or negative values. A positive value means that males score higher, and a negative value means that females score higher. To give a tangible example, the gender difference in throwing distance is + 1.98. In a meta-analysis, d is computed for each study, and then ds are averaged across all studies. Because meta-analysis aggregates over numerous studies, a meta-analysis typically represents the testing of tens of thousands, sometimes even millions of participants. Thus, the results should be far more reliable than those from any individual study. How do we know when a d, an effect size, is small or large? The statistician Jacob Cohen provided the guideline that a d of 0.20 is small, 0.50 is moderate, and 0.80 is large. their performance on standardized mathematics tests, because students experi- ence mathematical problem-solving in physics and chemistry classes. Concerning gender differences in verbal ability, meta-analysis of 165 studies representing the testing of 1.4 million persons showed superior performance by females but the difference is very small (d = –0.11).3 The question of gender differences in spatial ability, a relevant skill in many fields of science and engi- neering, is complicated because there are many types of spatial ability and many tests to measure them. With regard to gender differences in three dimensional 3JS Hyde and MC Linn (1988). Gender differences in verbal ability: A meta-analysis. Psychological Bulletin 104:53-69.

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13 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS mental rotation, which is crucial in fields such a engineering,4 two meta-analyses have been conducted. One found a large gender difference favoring males, and the other found a medium gender difference favoring males,5 both more substan- tial than for mathematical and verbal abilities. That does not mean that girls can- not succeed at engineering; research shows that spatial skills can be trained.6 One major factor in determining mathematics performance is student high school course choice. In investigating what factors influence adolescents’ choice of courses and careers, Eccles found that students value what they think they will learn in a course, and that is heavily influenced by intended career.7 Many occu- pations in the U.S. are highly gender-segregated. That makes it more likely that girls will not imagine themselves in science or engineering careers and therefore they will not value mathematics or physics courses as much as boys do. Parents play an important role. Research shows that even in elementary school, parents estimate the math ability of sons to be higher than those of daughters, despite the absence of any gender difference in actual grades or test scores at this point. One particularly impressive longitudinal study found that mothers’ estimates of their 6th grader’s likelihood of mathematics success predicted the child’s actual mathematics career choice at age 25.8 Schools play a third important role on the gender difference in advanced mathematics and science performance. Research shows, for example, that hands- on laboratory experiences in the physical sciences improved the science achieve- ment of girls but not of boys, and helped to close the gender gap in achievement. Cultural influences at the broadest level also play a role. In a cross-national study of 5th graders’ math performance,9 one could focus on the small difference in performance between girls compared with boys. However, the bigger picture 4M Hegarty and VK Sims (1994). Individual differences in mental animation during mechanical reasoning. Memory and Cognition 22(4):411-430. 5MC Linn and AC Petersen (1985). Emergence and characterization of sex differences in spatial ability: A meta-analysis. Child Development 56:1479-1498; D Voyer, S Voyer, and MP Bryden (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin 117:250-270. 6Hyde referred to a study by Sheryl Sorby and her colleagues, who have developed a multi-media software program that improves the spatial performance of students and has improved the retention of women in the engineering major from 47% to 77%. See: N Boersma, A Hamlin, and S Sorby (2005). Work in progress—Impact of a remedial 3-D visualization course on student performance and reten- tion. Presentation at 34th ASEE/IEEE Frontiers in Education Conference, October 20-23, 2004, Savannah, GA. http://fie.engrng.pitt.edu/fie2004/papers/1391.pdf. 7JS Eccles (1994). Understanding women’s educational and occupational choices: Applying the Eccles et al. model of achievement-related choices. Psychology of Women Quarterly 18:585-610. 8JE Jacobs and JS Eccles (1992). The influence of parent stereotypes on parent and child ability beliefs in three domains. Journal of Personality and Social Psychology 63(6):932-44. 9M Lummis and HW Stevenson (1990). Gender differences in beliefs and achievement: A cross- cultural study. Developmental Psychology 26:254-263.

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14 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING 20 18 16 14 World Problem Score 12 Boys 10 Girls 8 6 4 2 0 Taiwan Japan United States FIGURE 1-1 Cross-cultural differences in fifth-grade mathematics performance. SOURCE: M Lummis and HW Stevenson (1990). Gender differences in beliefs and achievement: A cross-cultural study. Developmental Psychology 26:254-263. shows that girls in Taiwan and Japan dramatically outperform American boys. Many features probably account for this, among them differences in the way mathematics is taught, in cultural values attached to mathematics, and in different attitudes about the importance of ability vs. effort in producing excellent performance. Another study looked at the magnitude of the gender difference in mathemat- ics performance in different countries and correlated it with the United Nations standardized measure of gender stratification.10 The correlation between math- ematics performance and the percentage of women in the paid workforce was an impressively large –0.55 across nations. Countries with the greatest gender strati- fication tended to have the largest gender difference favoring males. All those findings led Hyde to propose the gender similarities hypothesis.11 She subjected 46 relevant meta-analyses to a meta-analysis. The studies spanned a wide range of psychological characteristics, including abilities, communica- tion, aggression, leadership, personality and self-esteem. She found 78% of the gender differences effect sizes were small or close to 0. Psychologically, women and men are more similar than they are different. Large gender differences are found in a few cases, but the big picture is one of gender similarities. 10DP Baker and DP Jones (1993). Creating gender equality: Cross-national gender stratification and mathematical performance. Sociology of Education 66:91-103. 11JS Hyde (2005). The gender similarities hypothesis. American Psychologist 60:581-592.

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15 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS On the basis of these data, Hyde suggested some policy recommendations: (1) a spatial learning curriculum should be instituted in primary and secondary schools, (2) colleges of engineering should have a spatial skills training program for entering students, (3) four years of math and four years of science should be required in high school or at least for university admission, (4) the mathematics curriculum in many states needs far more emphasis on real problem solving, and (5) teachers and high school guidance counselors need to be educated about the findings on gender similarities in mathematics performance, or teachers will be- lieve the stereotypes about girls’ mathematics inferiority that pervade our culture and those expectations will be conveyed to the students. SEXUAL DIMORPHISM IN THE DEVELOPING BRAIN Jay Giedd National Institute of Mental Health, National Institutes of Health Jay Giedd began by noting his focus on the adolescent brain. In child psychiatry nearly everything has different prevalences, ages of onset, and symp- tomatology between boys and girls and nearly every disorder is more common in boys. His studies use MRI, magnetic resonance imaging, which because it does not require radiation can be used in children to perform longitudinal studies. To the MRI machine, the brain is boxes of gray or white measuring about 1 cubic millimeter. Within each of these boxes are millions of neurons and trillions of synaptic connections. Using much finer reso- lution microscopic techniques, one can see synapses and connections, but MRI currently cannot do that. MRI pictures and images can be quite colorful, but interpretations are necessarily speculative. —Jay Giedd What we call the gray matter consists mostly of the neuronal cell bodies, where the nucleus and the DNA are housed; the antenna-like dendrites reaching for connections to other brain cells; and the terminal branches of the axons, the location of the synapses, and the connections to other brain cells. The white matter is myelin, the insulation material wrapped around the axon that speeds communi- cation between the brain cells. Giedd and his colleagues performed longitudinal MRI scans of 2,000 sub- jects. They found that white matter volume increased at least through the fourth decade in women and through the third in men (Figure 1-2). At no time during development did white matter volume decrease. The white matter has been of interest in the study of sexual brain-structure differences, or sexual dimorphism, because one of the first reports of a brain difference not related to reproduction concerned the corpus callosum, the white

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16 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING 450 Volumn in cubic cm 400 350 Male (152 scans from 90 subjects) Female (91 scans from 55 subjects) 300 95% confidence interval-female 95% confidence interval–male - 250 4 6 8 10 12 14 16 18 20 22 Age FIGURE 1-2 Longitudinal development of white matter. SOURCE: JN Giedd, J Blumenthal, NO Jeffries, FX Castellanos, H Liu, A Zijdenbos, T Paus, AC Evans, and JL Rapaport (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience 2(10):861-863. matter tract connecting the two brain hemispheres. In over 100 published papers, the results are inconclusive—the corpus callosum of females is bigger than, smaller than, or not different from that of males. The key to understanding these results is in considering developmental windows. At young ages the corpus callosum is sexually dimorphic; between ages 9 and 14 it is not; and then it becomes so again. These changes happen throughout life. Brain areas have intersecting developmental trajectories. This is a very important concept in how to interpret the findings. Often, the litera- ture will combine data from people across seven or eight decades, and report that average as the difference between male and female brains. —Jay Giedd The most robust sex difference is total brain size. From autopsy studies, even when correcting for total body mass, male brains have been found to be about 10% larger than female brains, but bigger isn’t better and size is not related to intelligence. A lot of the literature is really murky on how to account for total brain size difference. The other part that MRI can see—the gray matter—has a distinct develop- mental trajectory from that of white matter. Instead of a general linear increase in volume, gray matter has an upside down “U” path in development. Changes in cortical thickness are not due to an increase in the number of neuronal cells, but to an increase in arborization, or the number of branches, twigs, and roots of exist- ing individual neurons. Although both progressive and regressive processes occur throughout life, during childhood there is a net increase in the degree of branch-

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17 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS ing and during adolescence there is a net decrease. Growth reaches a peak in the frontal part of the brain at 11 in girls and 14 years in boys. Pruning then begins: the cells and connections that are used survive and flourish, and those that are not wither and die. There is a lot of regional variation in the process. Maturation starts in the parts of the brain needed to keep us alive, such as those controlling heart rate and breathing. The next parts of the brain to mature are those involved in processing the five senses, followed by the parts of the brain that link together the primary senses. Then there is a cascade of hierarchies linking the linkings. The final stop is the frontal lobe, which doesn’t reach adult levels until about age 25. By adulthood, once you correct for the total brain-size differences, the sex differences are quite subtle. But if you look at the path the brains took to get there, the differences are far more robust. It’s the journey, not the destination. —Jay Giedd The most variable parts of the brain seem to be those that mature last, and are the least heritable. The structure that we have examined thus far that is the most different between males and females is the cerebellum. Because it is one of the last brain areas to mature, the cerebellum is under the influence of the environ- ment for a long period. Accounting for overall brain size increase, the cerebellum is larger and it reaches adult volume later in males than in females. Overall, male brains have a greater variation in cortical thickness; this is a very robust phenom- enon that occurs throughout the brain. Giedd summarized with two points: First, male and female adolescent brains are much more alike than different; there is enormous overlap. Second, with regard to developmental trajectories, there are more marked sex differences. Male brain structure appears more variable. Whether the variability is biological or social in origin, the data are robust. Work is underway on the effects of sex chromosomes and hormones. In ending, Giedd emphasized that differences are group average differences, and are not to be implied as constraints for individual boys or girls. ENVIRONMENT-GENETIC INTERACTIONS IN THE ADULT BRAIN: EFFECTS OF STRESS ON LEARNING Bruce McEwen The Rockefeller University Bruce McEwen presented data on sex-based differences in the effects of stress, which have implications for learning. He and his colleagues study brain regions that are involved in memory, emotions, and executive function or deci-

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86 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING Native American 3 18 Professor 4 10 Associate Professor (115) 28 27 Assistant Professor 9 9 Instructor 5 2 Other 141 606 Hispanic (4,537) Professor 254 603 Associate Professor 861 1,396 Assistant Professor 288 299 Instructor 44 45 Other 67 278 Professor Race/Ethnicity Black (3,536) 235 400 Associate Professor 937 989 Assistant Professor 325 228 Instructor 40 37 Other 326 1,568 Professor Asian (14,191) 686 1,729 Associate Professor 2,545 4,730 Assistant Professor 800 1,176 Instructor 235 396 Other 3,366 19,847 Professor White (81,874) 5,113 14,224 Associate Professor 11,057 18,693 Assistant Professor 3,939 3,921 Instructor 843 871 Other 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Women Men Percentage of Faculty FIGURE 1-16 Medical school faculty by rank, gender, race, and ethnicity. SOURCE: AAMC Faculty Roles Survey, 2004. Using elements of those themes, Reede described three Harvard Medical School programs that are related to organizational structures and policies that deal with collaboration and that address issues that cross multiple levels of the academic and career ladder. Biomedical Science Careers Program (BSCP). Of the students in this pro- gram 60% are women, 47% are African American, and 19% are Hispanic. Founded in 1991 with a group of people from Reede’s office at Harvard Medical School, the Massachusetts Medical Society, and the New England Board of Higher Education, the BSCP quickly grew to a community of individuals and organizations that shared a desire to address diversity. It is now led by a board of directors that includes presidents and CEOs in biotechnology, medical device research, legal, and finance industries; leaders in academe, professional associa- tions, and community colleges; educators; practitioners; and employers. Sup- ported by the community, and without public funding, the BSCP has now reached more than 4,800 high school, college, medical school and professional school graduates, and postdoctoral students and fellows. The more than 500 volunteers who have made the BSCP initiatives work point to the fact that many in the bio- medical community are deeply concerned about education, diversity, and the

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7000 Women Men 6000 Professor Associate Professor 5000 Assistant Professor Other Faculty Not Applicable 4000 3000 2000 1000 Number Employed in Universities and 4-year Colleges 0 Asian/Pacific Black Hispanic American Asian/Pacific Black Hispanic American Islander Indian/Alaskan Islander Indian/Alaskan Native Native Race/Ethnicity FIGURE 1-17 Number of science and engineering doctorate holders employed in science and engineering occupations in universities and 4-year colleges, by race, ethnicity, and faculty rank, 2001. SOURCE: Women, Minorities, and Persons with Disabilities in Science and Engineering (2004). Arlington, VA: National Science Foundation. Note: American Indian/Alaskan Native (AI/AN) data for “Other faculty” are suppressed because there are fewer than 50 weighted cases. 87

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88 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING future science workforce. BSCP students have said that experiential opportunities such as internships and job shadowing, contact with minority-group role models, and encouragement from teachers have a large influence on their educational and career goals. BSCP has influenced students to obtain more information, strengthen their interests, and to make them aware of career opportunities and connections with people. Students have been able to identify jobs and apply for jobs, partici- pate in new programs and internships, identify mentors at their schools, and obtain funding. Visiting Clerkship Program (VCP). More than 700 third- and fourth-year medical students from schools across the country have participated in this 1-month externship program, established in 1990. The program offers travel, housing, faculty advisers, and access to networks and resources. Some 15% of VCP students have returned to Harvard as residents, fellows, and faculty. The students have said the things that are important to them in selecting a residency program are academic training programs, the pre-eminence of those programs, their recommendations and interactions with advisers and mentors, their potential for research participation, and family considerations. Center of Excellence in Minority Health and Health Disparities. This fellow- ship program for junior faculty was established in 2002; the first cohort began in 2003. To date, nine faculty fellows have participated. Four have been promoted, one to a division chief. Two are up for promotion now. Eight have obtained exter- nal grant funding. An essential component of the program is selected mentors. All fellows have to have letters of support and involvement of department chairs, and the presidents of the participating Harvard Medical School hospitals are involved in the selection. Built into this program are accountability and recogni- tion of and support for excellence. What issues need to be addressed if we are to achieve racial and ethnic diversity? Responsibility at multiple levels. There needs to be top-down and bottom-up activity that provides vehicles to ensure the success of minority and women students, trainees, and faculty. And this activity must extend beyond verbal acknowledgment of the need for diversity. Recognition of the need should be incorporated into the institutional missions, reiterated in the setting of policies, and integrated in the design of programs. Data should be disaggregated to ensure that issues that disproportionately impact certain racial and ethnic groups are appropriately addressed and outcomes of interventions are adequately tracked. Diversity is not just about minorities and women. Diversity is about how we can improve and advance science for all. —Joan Reede

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89 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS CREATING AN INCLUSIVE WORK ENVIRONMENT90 Sue Rosser Ivan Allen College, Georgia Institute of Technology Sue Rosser began with an emphasis on the need for practical institutional approaches, as suggested by the MIT report.91 Almost simultaneous with the release of the MIT report, the National Science Foundation launched its ADVANCE Institutional Transformation Initiative. For many years, there had been successor programs such as visiting professorships for women, career advancement awards, and professional opportunities for women in research and education (POWRE). Although some of them had a component for institutional transformation, they largely gave money to individual women researchers. In contrast, ADVANCE focuses on institutional changes, especially for women on the academic tenure track to senior and leadership positions. The first round of ADVANCE awardees occurred in 2001 and the second in 2003, and the third round will be announced in early 2006.92 From these grants will come several models of what has worked and what has not worked for different institutions. Rosser studied the NSF POWRE awardees and the Clare Booth Luce professors to understand the most significant issues, challenges, and opportunities facing women scientists today as they plan their careers.93 She received about 450 responses to e-mail questionnaires and conducted 40 in-depth interviews. Respondents were distributed among all the disciplines, and each of the different years the awards were made are represented. The first question—an open-ended question—was, What are the most significant issues, challenges, and opportunities facing women scien- tists today as they plan their careers? People could have said anything. What amazed me was that balancing career with family was the over- whelming response—65 to 88% for all 4 years. —Sue Rosser After balancing career with family, a second major issue was time manage- ment: balancing work with research, teaching, and service. The third issue was isolation: low numbers, and lack of camaraderie and mentoring. The fourth issue 90For more details, figures, and references, see the paper by Rosser in Section 2. 91Massachusetts Institute of Technology (1999). A study on the status of women faculty in science at MIT. The MIT Faculty Newsletter 11(4):14-26, http://web.mit.edu/fnl/women/women.html. 92Several of the meeting posters presented research from ADVANCE grantees; see the poster abstracts later in this volume (p. 175). 93SV Rosser and J Daniels (2004). Widening paths to success, improving the environment, and moving toward lessons learned from experiences of POWRE and CBL awardees. Journal of Women and Minorities in Science and Engineering 10(2):131-148.

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90 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING was gaining credibility and respectability. And then the fifth major issue was the dual career problem. All those issues have come up previously in this meeting. Many of these issues are centered around the fact that the life cycle is based on what I call the white male model. There is nothing wrong with that, unless you’re not white and male; if you are not then it does not work very well for you, particularly the competition between the biological clock and the tenure clock. —Sue Rosser Another way of presenting the data is by dividing the responses into four groups: 1. Pressures women face in balancing career and family (~30%). 2. Problems faced by women because of low numbers and stereotypes held by others regarding gender (~10%). 3. Issues that are faced by both men and women scientists and engineers which, because of the current environment of tight resources, may pose particular difficulties for women (~7%). 4. Overt discrimination and harassment (~5%). Included in the first category are issues related to the dual career family, which is a particular problem because most women scientists and engineers are married to men scientists and engineers. The second category has to do with being taken seriously and having increased visibility. The latter is particularly important for women of color, who are very visible because of their low numbers in faculties. If things go well, that’s remembered and can put you on a quick career trajectory. If things go badly, it is not forgotten. The third category contains issues that are faced by everyone but that have particular angles for women, such as the assumption of being available. Finally there are overt discrimination and harassment, including slow pro- motions, lack of women in senior positions, and placement of women into diffi- cult situations because they must buffer the bad behavior of their male colleagues. Effective models for countering some of those issues have been developed and tested at some of the ADVANCE institutions, including • Family-friendly policies and practices, including family-work initiatives, tenure-clock extension, childbearing and family leave, active service modified duty, and daycare facilities. • Training of search committees. • Training of chairs and deans to manage search committee results and to foster a welcoming departmental environment. • Speed mentoring.

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91 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS Rosser explained that the Georgia Institute of Technology, an ADVANCE institution, has focused on training of tenure and promotion committees. It devel- oped an ADEPT model, an interactive CD-ROM with which people can partici- pate in a tenure and promotion meeting. It developed nine case studies and nine virtual CVs to go with them. The player of the game can participate with three virtual people in the tenure meeting; and depending on what the player says, ADEPT sends the conversation in a particular direction. In addition to research expertise, ADEPT includes such issues as gender, disability, race, ethnicity, and sexuality. The deans were the first to use ADEPT, and then it moved to the depart- ment level, the chairs, and all the promotion and tenure committees. All faculty are now using ADEPT. Rosser and her colleagues are now developing a “navigate your career” section for junior faculty, with frequently asked questions, such as, Should I serve on that NSF review panel, or should I be writing my own proposal? How do I decline gracefully to serve on that committee that my senior colleague has asked me to serve on? “Speed mentoring” is another popular program at the Georgia Institute of Technology. Junior faculty take their CVs to a meeting with senior faculty who have served on tenure and promotion committees, but who are not currently on tenure and promotion committees. The junior faculty meet with four or five senior faculty in an hour to get a quick take on their CVs and what they might need to do to get ready for promotion to the next level. The senior faculty may suggest another publication or two in a refereed journal, beefing up their teaching, more service on national committees, and so on. Junior faculty like this very much, and say that they get an impression of the different perspectives that different people have. These are the people who have served on tenure and promotion commit- tees, so it is quite realistic. Rosser is doing some research on older women scientists because she has become concerned that many of the policies put into place through ADVANCE are primarily for younger women. It is very important for more junior women to achieve tenure, but there are different problems for senior women that need to be addressed. SUCCESSFUL PRACTICES IN INDUSTRY Kellee Noonan Diversity Program Manager, Technical Career Path, Hewlett Packard Kellee Noonan explained that about 5 years ago, when Hewlett Packard (HP) merged with Compaq, they found that they had noncompatible technical career ladders. They took the opportunity to step back and say, “Can we take the former HP program and the former Compaq program, do some industry benchmarking,

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92 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING and put together a framework that gives us a place for technologists to be able to see where they are in terms of their skills and abilities, and what they need to be able to do to move to the next level?” In creating the Technical Career Program (TCP), the goals were to make the promotion process fair and transparent and to eliminate the cumulative bias in selection and promoting. The TCP appears to be helping in terms of moving women and, in the United States, members of underrepresented groups up the career ladder. The first law of diversity is that when bad things happen, they happen worse to people who are not in the majority. —Kellee Noonan, quoting Alan Fisher, iCarnegie, Co-author of “Unlocking the Clubhouse” In developing a promotion policy, HP is faced with some challenges differ- ent from universities. For example, because it is a global company, the frame- work needs to apply to all its businesses in the whole world. HP has research laboratories in the United States, Israel, the United Kingdom, China, Brazil, and India. And it has researchers in outposts in many other places. The program has to be able to address all those areas. The TCP has clearly defined steps, and they are criteria-based. An employee can go to the TCP Web site in the company portal, and ask, What are the criteria to get to the next step? What kinds of things do I have to do? The criteria are balanced around three areas: impact on the business, depth and breadth of knowl- edge, and technical leadership skills. It’s a balance of all of them. You don’t have to be perfect in every one. You might have a technologist who is an inch deep but a mile wide. Or you could have a technologist who is an inch wide and a mile deep. What’s the difference? How do you evaluate that? The difference is in the impact they have on the business. How are they applying what they are doing to move the business forward and to get the best products, the best technology, and the best services out to customers in a way that matters? In addition to the criteria, which are specific, there are career development road maps, examples at every level of what it means to meet a criterion. An HP team works diligently to go through the company’s workforce-development resources to find the exact resources that match a criterion for each level, and publishes them on the company Web site. If my manager told me that I need to increase my ability to influence a negotiation when there is technology involved, I can go to the TCP Portal Web site and look it up under “influencing” or “negotiation” for my level. And I can find classes that are geared toward improving that skill. We have review boards in place at the higher levels of our ladder. If you get to what we call our strategists’ level, you are reviewed

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93 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS by a cross-business review board to make sure that we maintain con- sistency in the application of the levels across the company. —Kellee Noonan The TCP program core team has a representative of every chief technology office (CTO) to ensure consistency of policy and interpretation. The core team has an information technology function and a research and development function, called the Office of Strategy and Technology. It organizes quarterly TCP infor- mation forums, called “airing the dirty linen time,” for employees, HR, managers, and review boards. People can get to the really hard questions. This year, one major focus is the worldwide diversity team. Now, diversity goals and metrics are presented to the CTO every quarter. HP has also instituted technical leadership curriculum94 which is for distin- guished and master level technologists. Those leadership programs have taken traditional leadership out of the management curriculum. These new programs are focused on leading by influence and engagement, because most of the people on the technical career path don’t have a direct staff that can work with them or for them. They have to convince people that it’s worth working for them, they have to convince their manager, they have to convince their manager’s manager. We have broken the glass ceiling at some of our levels, but we still need to continue raising the roof. —Kellee Noonan SELECTIONS FROM THE QUESTION AND ANSWER SESSION DR. REED: Alyson Reed with the National Post-Doctoral Association. I was hoping that Sue Rosser could elaborate briefly on the speed mentoring model. DR. ROSSER: This is a neat program that one of our ADVANCE professors, Jane Ammons, invented. In an hour, junior faculty meet with four or five senior faculty—who have served on tenure and promotion committees, but who are not on tenure and promotion committees at the time of the meeting—and get a quick take on their CV and the senior faculty member’s concept of what they might need to do to get ready for promotion to the next level, from another publication or two in a refereed journal, to teaching, or service on national committees. It’s been very popular. We have done it now three or four times, and we have calls for more. I highly recommend it. The only issue is getting it organized, but we have 94The curriculum is based in part on Robert Kelley’s Star at Work (Three Rivers, MI: Three Rivers Press, 1999) and a program developed in-house called TCP Catalyst.

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94 COMPONENTS OF SUCCESS FOR WOMEN IN ACADEMIC SCIENCE & ENGINEERING not had trouble getting senior faculty to do it, and the junior faculty really, really like it. DR. FLETCHER: Hi, my name is Mary Ann Fletcher at the University of Miami. I would like to speak just briefly about the problem with recruitment of faculty. In my opinion, this problem frequently lies in the fact that at many universities, including my own, have a very severe shortage of women in higher positions in the administration. We have few women as deans, few women as chairs of departments. And so, this leads to search committees that I think frequently have an inborn bias. I serve on the faculty senate, and we recently had a provost search. I was able to convince our senate to set up a search committee that had one-half female faculty members, and one-half males. Some of the senators said, well, the faculty is not that way. And I said, but the student body is. There are more women than men in our student body. It takes work all the time, and I think the search com- mittees are really key. DR. AGOGINO: Alice Agogino from the University of California at Berkeley. Do you have any recommendations on how to improve the climate for women faculty in those departments that haven’t reached this magical 18% number? DR. STACY: We really do need to start taking action at the department level, especially when we start to hear that they are not being managed well, or the interactions aren’t productive, and that they are not an inclusive environment for all the members of that unit, including students and staff, as well as faculty. I think we just need the wherewithal to say that mismanagement is not acceptable at our institutions. DR. ROSSER: One of the things that’s been helpful to us at Georgia Tech with the ADVANCE institutional transformation grant is in each college we have an ADVANCE professor. This senior professor gets paid extra money from the grant, on the order of $60,000 a year—it’s like an endowed chair—to do activities and build mentoring networks. That has united women in each college, so that for example in engineering, where there are something like 415 tenure track faculty and where women may be isolated in departments, they now don’t feel as isolated, because they now know each other across the college. I have also encouraged department chairs to encourage their women faculty to join women’s studies, which most science chairs think, huh? Why would they do that? I say that may be what makes women hang in. DR. REEDE: I just want to speak a little bit related to that question, and the comment before about representation of women on searches, et cetera, because one of the critical issues here is oftentimes what gets left out of these discussions: minority representation. What ends up happening is that one minority person in that department gets rotated for everything. My challenge to the committee and to all of you, as you go back to your institutions, as you think about issues of women, don’t ignore the issues of women of color. When you think about putting women on committees,

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95 SECTION 1: SUMMARIES OF CONVOCATION SESSIONS also think about who will be attending to issues for women and men of color. If you are waiting until women and men of color reach a critical mass on faculties, then I’ll have to look at my great-grandchildren. Oftentimes with minorities, issues are not going to bubble up to the top. They are so isolated and so alienated you may not hear about it. DR. HAZELTINE: Florence Hazeltine, National Institutes of Health. You said that that there was a bias that women would get short listed or interviewed, but not get past the next step. This reminded me of some business models where if a woman at a high level lost her job, it took her twice as long to get a job as a man. What I want to know is when women do get interviewed for high-level academic jobs—and I see women presidents and chancellors—how many times have they gone up for it versus how many times the successful men have. If the women knew that in advance, we might be able to get them better coaching, a better feel for the system, when they are just going for practice, and when they are just going for real. DR. KEOHANE: Having had prior experience either in a coaching setting or having been through another interview is very important. I know that this is true for many men as well as women. Since our particular focus here is for women, I think that the suggestion that was implicit in your question is that you prepare in advance for a job interview in the same way you did as a graduate student when you were going for your first assistant professorship, and you learn from these experiences. I don’t know that there is any evidence that women in most of the really top positions are more likely to be short listed and not chosen. I think that was often true in the past. I think women are more often getting into top leadership posi- tions when they are on the final choice set. What I worry about is that not enough women in academia are seeing high-level leadership positions as an appropriate ambition for themselves. One of the things I find most encouraging about this congregation today is the number of women of strong faculty backgrounds who have been willing to say I will be an associate dean, I will be an associate provost, I will be a provost or a dean or a president.

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