Creating Equitable Research Collaborations

This tool–Creating equitable research collaborations–is part 1 of a three tool series for embedding equity into all phases of research collaboration. See also Continuing equitable collaborative relationships (part 2) and Crediting collaboration equitably (part 3)

Continuing Research Collaboration Relationships

This tool–Continuing equitable research collaboration relationships–is part 2 of a three tool series for embedding equity into all phases of research collaboration. See also Creating equitable research collaborations (part 1) and Crediting collaboration equitably (part 3)

Crediting Collaboration Equitably

This tool–Crediting collaboration equitably–is part 3 of a three-tool series for embedding equity into all phases of research collaboration. See also Creating equitable research collaborations (part 1) and Continuing equitable collaboration relationships (part 2)

Chairs Checklist for Shared Decision Making

UMass ADVANCE survey results indicate that women faculty are less clear on personnel processes than men, and women faculty members from underrepresented racial minority groups are the least clear on tenure and promotion processes. This checklist is for department chairs/heads to assess departmental shared decision-making and create equitable practices that will support the inclusion and retention of women faculty and faculty of color

Setting the Stage for Equitable Faculty Shared Decision-Making

The most consequential decisions regarding faculty careers are decided collectively by peers, including tenure, promotions, and annual merit pay raises. The shared decision making inherent in faculty governance is a type of collaboration that faculty experience in unequal ways. While most departments have formal procedures and written policies, academia remains dominated by informal ways of functioning that allow gender and racial inequalities to persist. UMass ADVANCE survey results indicate that women faculty are less clear on personnel processes than men, and women faculty members from underrepresented racial minority groups are the least clear on tenure and promotion processes. Women faculty are often uncertain about their next career steps. Creating equitable practices around shared decision-making will improve transparency and trust among colleagues, supporting the inclusion and retention of women faculty and faculty of color, especially those at junior ranks. How can governance be reinvented to be more equitable for women faculty and faculty of color

A critical perspective on second-order empathy in understanding psychopathology: phenomenology and ethics

The centenary of Karl Jaspers’ General Psychopathology was recognised in 2013 with the publication of a volume of essays dedicated to his work (edited by Stanghellini and Fuchs). Leading phenomenological-psychopathologists and philosophers of psychiatry examined Jaspers notion of empathic understanding and his declaration that certain schizophrenic phenomena are ‘un-understandable’. The consensus reached by the authors was that Jaspers operated with a narrow conception of phenomenology and empathy and that schizophrenic phenomena can be understood through what they variously called second-order and radical empathy. This article offers a critical examination of the second-order empathic stance along phenomenological and ethical lines. It asks: (1) Is second-order empathy (phenomenologically) possible? (2) Is the second-order empathic stance an ethically acceptable attitude towards persons diagnosed with schizophrenia? I argue that second-order empathy is an incoherent method that cannot be realised. Further, the attitude promoted by this method is ethically problematic insofar as the emphasis placed on radical otherness disinvests persons diagnosed with schizophrenia from a fair chance to participate in the public construction of their identity and, hence, to redress traditional symbolic injustices

Nanotechnology researchers' collaboration relationships: A gender analysis of access to scientific information

Women are underrepresented in science, technology, engineering, and mathematics fields, particularly at higher levels of organizations. This article investigates the impact of this underrepresentation on the processes of interpersonal collaboration in nanotechnology. Analyses are conducted to assess: (1) the comparative tie strength of women's and men's collaborations, (2) whether women and men gain equal access to scientific information through collaborators, (3) which tie characteristics are associated with access to information for women and men, and (4) whether women and men acquire equivalent amounts of information by strengthening ties. Our results show that the overall tie strength is less for women's collaborations and that women acquire less strategic information through collaborators. Women and men rely on different tie characteristics in accessing information, but are equally effective in acquiring additional information resources by strengthening ties. This article demonstrates that the underrepresentation of women in science, technology, engineering, and mathematics has an impact on the interpersonal processes of scientific collaboration, to the disadvantage of women scientists.Villanueva-Felez, Á.; Woolley, RD.; Cañibano Sánchez, C. (2015). Nanotechnology researchers' collaboration relationships: A gender analysis of access to scientific information. Social Studies of Science. 45(1):100-129. doi:10.1177/0306312714552347S100129451ACKER, J. (1990). HIERARCHIES, JOBS, BODIES: Gender & Society, 4(2), 139-158. doi:10.1177/089124390004002002Aitken, C., Power, R., & Dwyer, R. (2008). A very low response rate in an on-line survey of medical practitioners. Australian and New Zealand Journal of Public Health, 32(3), 288-289. doi:10.1111/j.1753-6405.2008.00232.xAngrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics. doi:10.1515/9781400829828Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in organizational research. Human Relations, 61(8), 1139-1160. doi:10.1177/0018726708094863Beaver, D. D. (2001). Scientometrics, 52(3), 365-377. doi:10.1023/a:1014254214337Boardman, P. C., & Corley, E. A. (2008). University research centers and the composition of research collaborations. Research Policy, 37(5), 900-913. doi:10.1016/j.respol.2008.01.012Bourdieu, P. (1975). The specificity of the scientific field and the social conditions of the progress of reason. Social Science Information, 14(6), 19-47. doi:10.1177/053901847501400602Bourdieu, P. (1977). Outline of a Theory of Practice. doi:10.1017/cbo9780511812507Bourdieu, P. (1989). Social Space and Symbolic Power. Sociological Theory, 7(1), 14. doi:10.2307/202060Bouty, I. (2000). INTERPERSONAL AND INTERACTION INFLUENCES ON INFORMAL RESOURCE EXCHANGES BETWEEN R&D RESEARCHERS ACROSS ORGANIZATIONAL BOUNDARIES. Academy of Management Journal, 43(1), 50-65. doi:10.2307/1556385Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: implications for scientific and technical human capital. Research Policy, 33(4), 599-616. doi:10.1016/j.respol.2004.01.008Bozeman, B., & Gaughan, M. (2011). How do men and women differ in research collaborations? An analysis of the collaborative motives and strategies of academic researchers. Research Policy, 40(10), 1393-1402. doi:10.1016/j.respol.2011.07.002Bozeman, B., & Rogers, J. D. (2002). A churn model of scientific knowledge value: Internet researchers as a knowledge value collective. Research Policy, 31(5), 769-794. doi:10.1016/s0048-7333(01)00146-9Bozeman, B., Dietz, J. S., & Gaughan, M. (2001). Scientific and technical human capital: an alternative model for research evaluation. International Journal of Technology Management, 22(7/8), 716. doi:10.1504/ijtm.2001.002988Brass, D. J. (1985). MEN’S AND WOMEN’S NETWORKS: A STUDY OF INTERACTION PATTERNS AND INFLUENCE IN AN ORGANIZATION. Academy of Management Journal, 28(2), 327-343. doi:10.2307/256204Chompalov, I., Genuth, J., & Shrum, W. (2002). The organization of scientific collaborations. Research Policy, 31(5), 749-767. doi:10.1016/s0048-7333(01)00145-7Cook, C., Heath, F., & Thompson, R. L. (2000). A Meta-Analysis of Response Rates in Web- or Internet-Based Surveys. Educational and Psychological Measurement, 60(6), 821-836. doi:10.1177/00131640021970934Durbin, S. (2010). Creating Knowledge through Networks: a Gender Perspective. Gender, Work & Organization, 18(1), 90-112. doi:10.1111/j.1468-0432.2010.00536.xEcklund, E. H., Lincoln, A. E., & Tansey, C. (2012). Gender Segregation in Elite Academic Science. Gender & Society, 26(5), 693-717. doi:10.1177/0891243212451904Ensign, P. C. (2009). Knowledge Sharing among Scientists. doi:10.1057/9780230617131Etzkowitz, H., Kemelgor, C., & Uzzi, B. (2000). Athena Unbound. doi:10.1017/cbo9780511541414Flyvbjerg, B. (2001). Making Social Science Matter. doi:10.1017/cbo9780511810503FOX, M. F. (2001). WOMEN, SCIENCE, AND ACADEMIA. Gender & Society, 15(5), 654-666. doi:10.1177/089124301015005002Fox, M. F. (2010). Women and Men Faculty in Academic Science and Engineering: Social-Organizational Indicators and Implications. American Behavioral Scientist, 53(7), 997-1012. doi:10.1177/0002764209356234Fox, M. F., & Stephan, P. E. (2001). Careers of Young Scientists: Social Studies of Science, 31(1), 109-122. doi:10.1177/030631201031001006Fox, M. F., Sonnert, G., & Nikiforova, I. (2009). Successful Programs for Undergraduate Women in Science and Engineering: Adapting versus Adopting the Institutional Environment. Research in Higher Education, 50(4), 333-353. doi:10.1007/s11162-009-9120-4Friedkin, N. (1980). A test of structural features of granovetter’s strength of weak ties theory. Social Networks, 2(4), 411-422. doi:10.1016/0378-8733(80)90006-4Gaughan, M. (2005). Introduction to the Symposium: Women in Science. The Journal of Technology Transfer, 30(4), 339-342. doi:10.1007/s10961-005-2579-zGaughan, M., & Corley, E. A. (2010). Science faculty at US research universities: The impacts of university research center-affiliation and gender on industrial activities. Technovation, 30(3), 215-222. doi:10.1016/j.technovation.2009.12.001Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360-1380. doi:10.1086/225469Hansen, M. T. (1999). The Search-Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits. Administrative Science Quarterly, 44(1), 82. doi:10.2307/2667032Ibarra, H. (1992). Homophily and Differential Returns: Sex Differences in Network Structure and Access in an Advertising Firm. Administrative Science Quarterly, 37(3), 422. doi:10.2307/2393451Islam, N., & Miyazaki, K. (2009). Nanotechnology innovation system: Understanding hidden dynamics of nanoscience fusion trajectories. Technological Forecasting and Social Change, 76(1), 128-140. doi:10.1016/j.techfore.2008.03.021Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1-18. doi:10.1016/s0048-7333(96)00917-1Koch, N. S., & Emrey, J. A. . (2001). The Internet and Opinion Measurement: Surveying Marginalized Populations. Social Science Quarterly, 82(1), 131-138. doi:10.1111/0038-4941.00012Kyvik, S., & Teigen, M. (1996). Child Care, Research Collaboration, and Gender Differences in Scientific Productivity. Science, Technology, & Human Values, 21(1), 54-71. doi:10.1177/016224399602100103Lee, S., & Bozeman, B. (2005). The Impact of Research Collaboration on Scientific Productivity. Social Studies of Science, 35(5), 673-702. doi:10.1177/0306312705052359Levin, D. Z., & Cross, R. (2004). The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer. Management Science, 50(11), 1477-1490. doi:10.1287/mnsc.1030.0136Lin, N. (2001). Social Capital. doi:10.1017/cbo9780511815447McFadyen, M. A., & Cannella, A. A. (2004). SOCIAL CAPITAL AND KNOWLEDGE CREATION: DIMINISHING RETURNS OF THE NUMBER AND STRENGTH OF EXCHANGE RELATIONSHIPS. Academy of Management Journal, 47(5), 735-746. doi:10.2307/20159615McFadyen, M. A., Semadeni, M., & Cannella, A. A. (2009). Value of Strong Ties to Disconnected Others: Examining Knowledge Creation in Biomedicine. Organization Science, 20(3), 552-564. doi:10.1287/orsc.1080.0388Manfreda, K. L., Bosnjak, M., Berzelak, J., Haas, I., & Vehovar, V. (2008). Web Surveys versus other Survey Modes: A Meta-Analysis Comparing Response Rates. International Journal of Market Research, 50(1), 79-104. doi:10.1177/147078530805000107Marsden, P. V., & Campbell, K. E. (1984). Measuring Tie Strength. Social Forces, 63(2), 482. doi:10.2307/2579058Mason, M. A., & Ekman, E. M. (2007). Mothers on the Fast TrackHow a New Generation Can Balance Family and Careers. doi:10.1093/acprof:oso/9780195182675.001.0001Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model Of Organizational Trust. Academy of Management Review, 20(3), 709-734. doi:10.5465/amr.1995.9508080335Nahapiet, J., & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational Advantage. Academy of Management Review, 23(2), 242-266. doi:10.5465/amr.1998.533225Oliver, A. L., & Liebeskind, J. P. (1997). Three Levels of Networking for Sourcing Intellectual Capital in Biotechnology. International Studies of Management & Organization, 27(4), 76-103. doi:10.1080/00208825.1997.11656719Podolny, J. M., & Baron, J. N. (1997). Resources and Relationships: Social Networks and Mobility in the Workplace. American Sociological Review, 62(5), 673. doi:10.2307/2657354Rhoton, L. A. (2011). Distancing as a Gendered Barrier. Gender & Society, 25(6), 696-716. doi:10.1177/0891243211422717Rothstein, M. G., & Davey, L. M. (1995). Gender differences in network relationships in academia. Women in Management Review, 10(6), 20-25. doi:10.1108/09649429510095999Rowley, T., Behrens, D., & Krackhardt, D. (2000). Redundant governance structures: an analysis of structural and relational embeddedness in the steel and semiconductor industries. Strategic Management Journal, 21(3), 369-386. doi:10.1002/(sici)1097-0266(200003)21:33.0.co;2-mSCOTT, D. B. (1996). SHATTERING THE INSTRUMENTAL-EXPRESSIVE MYTH. Gender & Society, 10(3), 232-247. doi:10.1177/089124396010003003Shapin, S. (1994). A Social History of Truth. doi:10.7208/chicago/9780226148847.001.0001Shrum, W., Chompalov, I., & Genuth, J. (2001). Trust, Conflict and Performance in Scientific Collaborations. Social Studies of Science, 31(5), 681-730. doi:10.1177/030631201031005002Smith-Doerr, L. (2004). Flexibility and Fairness: Effects of the Network Form of Organization on Gender Equity in Life Science Careers. Sociological Perspectives, 47(1), 25-54. doi:10.1525/sop.2004.47.1.25Stix, G. (2001). Little Big Science. Scientific American, 285(3), 32-37. doi:10.1038/scientificamerican0901-32Uzzi, B. (1996). The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect. American Sociological Review, 61(4), 674. doi:10.2307/2096399Uzzi, B. (1997). Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. Administrative Science Quarterly, 42(1), 35. doi:10.2307/2393808Vinck, D. (2010). The Sociology of Scientific Work. doi:10.4337/9781849807197Fan, W., & Yan, Z. (2010). Factors affecting response rates of the web survey: A systematic review. Computers in Human Behavior, 26(2), 132-139. doi:10.1016/j.chb.2009.10.015Ziman, J. M. (Ed.). (1994). Prometheus Bound. doi:10.1017/cbo978051158506

Measurement of the Lambda_b Lifetime in Lambda_b --> J/psi Lambda0 in p-pbar Collisions at sqrt(s)=1.96 TeV

We report a measurement of the Lambda_b lifetime in the exclusive decay Lambda_b --> J/psi Lambda0 in p-pbar collisions at sqrt(s) = 1.96 TeV using an integrated luminosity of 1.0 fb^{-1} of data collected by the CDF II detector at the Fermilab Tevatron. Using fully reconstructed decays, we measure tau(Lambda_b) = 1.593 ^{+0.083}_{-0.078} (stat.) +- 0.033 (syst.) ps. This is the single most precise measurement of tau(Lambda_b) and is 3.2 sigma higher than the current world average.Comment: 7 Pages, 2 Figures, 1 Table. Submitted to Phys. Rev. Let

Measurement of the Ratios of Branching Fractions B(Bs -> Ds pi pi pi) / B(Bd -> Dd pi pi pi) and B(Bs -> Ds pi) / B(Bd -> Dd pi)

Using 355 pb^-1 of data collected by the CDF II detector in \ppbar collisions at sqrt{s} = 1.96 TeV at the Fermilab Tevatron, we study the fully reconstructed hadronic decays B -> D pi and B -> D pi pi pi. We present the first measurement of the ratio of branching fractions B(Bs -> Ds pi pi pi) / B(Bd -> Dd pi pi pi) = 1.05 pm 0.10 (stat) pm 0.22 (syst). We also update our measurement of B(Bs -> Ds pi) / B(Bd -> Dd pi) to 1.13 pm 0.08 (stat) pm 0.23 (syst) improving the statistical uncertainty by more than a factor of two. We find B(Bs -> Ds pi) = [3.8 pm 0.3 (stat) pm 1.3 (syst)] \times 10^{-3} and B(Bs -> Ds pi pi pi) = [8.4 pm 0.8 (stat) pm 3.2 (syst)] \times 10^{-3}.Comment: 7 pages, 2 figure