LEADER 03466nam 2200637 a 450 001 9910790596003321 005 20230124190214.0 010 $a0-8147-7153-X 010 $a0-8147-7152-1 024 7 $a10.18574/9780814771525 035 $a(CKB)2670000000151356 035 $a(EBL)865873 035 $a(OCoLC)778459084 035 $a(SSID)ssj0000632841 035 $a(PQKBManifestationID)11392444 035 $a(PQKBTitleCode)TC0000632841 035 $a(PQKBWorkID)10610885 035 $a(PQKB)11083071 035 $a(StDuBDS)EDZ0001326775 035 $a(MiAaPQ)EBC865873 035 $a(OCoLC)785785330 035 $a(MdBmJHUP)muse19820 035 $a(DE-B1597)548234 035 $a(DE-B1597)9780814771525 035 $a(Au-PeEL)EBL865873 035 $a(CaPaEBR)ebr10535659 035 $a(OCoLC)1017995096 035 $a(EXLCZ)992670000000151356 100 $a20110922d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBreaking into the lab$b[electronic resource] $eengineering progress for women in science /$fSue V. Rosser 210 $aNew York $cNew York University Press$dc2012 215 $a1 online resource (262 p.) 300 $aDescription based upon print version of record. 311 $a1-4798-0920-9 311 $a0-8147-7645-0 320 $aIncludes bibliographical references and index. 327 $aAcknowledgments -- Introduction : why women in science are still controversial after thirty years -- Starting careers : plus ca change, plus c'est la meme chose -- Positive interventions from mentors and mentoring networks -- New filters for senior women scientists -- Advancing women scientists to senior leadership positions -- The gender gap in patents -- The impact that women have made on science and technology -- Conclusion: women in science are critical for society -- Appendix A: grants to support women scientists cited in this book -- Bibliography -- Index -- About the author. 330 $aWhy are there so few women in science? In Breaking into the Lab, Sue Rosser uses the experiences of successful women scientists and engineers to answer the question of why elite institutions have so few women scientists and engineers tenured on their faculties. Women are highly qualified, motivated students, and yet they have drastically higher rates of attrition, and they are shying away from the fields with the greatest demand for workers and the biggest economic payoffs, such as engineering, computer sciences, and the physical sciences. Rosser shows that these continuing trends are not only disappointing, they are urgent: the U.S. can no longer afford to lose the talents of the women scientists and engineers, because it is quickly losing its lead in science and technology. Ultimately, these biases and barriers may lock women out of the new scientific frontiers of innovation and technology transfer, resulting in loss of useful inventions and products to society. 606 $aWomen scientists$zUnited States 606 $aSex discrimination in science$zUnited States 615 0$aWomen scientists 615 0$aSex discrimination in science 676 $a500.82/0973 700 $aRosser$b Sue Vilhauer$0987657 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910790596003321 996 $aBreaking into the lab$93799208 997 $aUNINA LEADER 02424nam 2200529 a 450 001 9910791326403321 005 20161219111700.0 010 $a1-4833-4942-X 010 $a1-4522-3619-4 010 $a1-4522-2338-6 035 $a(CKB)2550000001197924 035 $a(EBL)1598333 035 $a(SSID)ssj0001111636 035 $a(PQKBManifestationID)12444658 035 $a(PQKBTitleCode)TC0001111636 035 $a(PQKBWorkID)11156376 035 $a(PQKB)11497194 035 $a(MiAaPQ)EBC1598333 035 $a(OCoLC)869282161 035 $a(StDuBDS)EDZ0000174331 035 $a(EXLCZ)992550000001197924 100 $a20131121d2011 fy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aComputational modeling in cognition$b[electronic resource] $eprinciples and practice /$fStephan Lewandowsky and Simon Farrell 210 $aThousand Oaks, [Calif.] $cSAGE$dc2011 215 $a1 online resource (x, 359 p.) $cill 300 $aDescription based upon print version of record. 311 $a1-322-28341-9 311 $a1-4129-7076-8 320 $aIncludes bibliographical references and index. 327 $aCover; Contents; Preface; Chapter 1 - Introduction; Chapter 2 - From Words to Models: Building a Toolkit; Chapter 3 - Basic Parameter Estimation Techniques; Chapter 4 - Maximum Likelihood Estimation; Chapter 5 - Parameter Uncertainty and Model Comparison; Chapter 6 - Not Everything That Fits Is Gold: Interpreting the Modeling; Chapter 7 - Drawing It All Together: Two Examples; Chapter 8 - Modeling in a Broader Context; References; Author Index; Subject Index; About the Authors 330 8 $aThis title introduces the principles of using computational models in psychology and provides a clear idea about how model construction, parameter estimation and model selection are carried out in practice. The book is written at a level that permits readers with a background in cognition, but without any modelling expertise. 606 $aCognition$xMathematical models 615 0$aCognition$xMathematical models. 676 $a153.015118 700 $aLewandowsky$b Stephan$01523339 701 $aFarrell$b Simon$f1976-$01523340 801 0$bStDuBDS 801 1$bStDuBDS 906 $aBOOK 912 $a9910791326403321 996 $aComputational modeling in cognition$93763502 997 $aUNINA