Banindo o preconceito entre disciplinas, gêneros e experiências - nova ferramenta para manãtricas de pesquisa mais justas
Chamado de andice Epsilon , em homenagem a letra grega ε usada para simbolizar resíduos nas estatasticas, ele leva em consideraça£o as muitas diferenças no espaço de pesquisa para fornecer uma comparaça£o mais justa.
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gzmNT0FNb07CFr3y1RX1xCtCMcS3/KJ4rxG9V4jbujrtiNMueoGjLZ1DSS8ReYWsrpdrGr+6aPRHJIKBPRYeNG9CEbMsDbLP33UWAoLoXc4lhdXmp+14j7p95kIqL0YDn2lnXbI5rKVqNOizwAXouY8TUJOjhah4UAE+T+JsjqfAJv6vunP0JZQvIMW2DRh7ah2qwW24PWJ3tJZfR+X5gcyy9nf+2LuABsLh7z8J8R94w9SF3Z1hxmDbWSBjdkS4wqmfLALhH0ZYuDRq7uzEB1pF2qQGOoqB3udCzTHrBcFGbVXaywMoQ8cHPrH145lM6HPmePDzNRb7nEPeFmRvuZMLYZY0J28czhCaV/3eD4gzs9FI06DgleEouV6y4JE2XqIaX9J/ibbttcXbM1Z7+kbKswatO0LB+r/AAWo9oDw4zNh66gqHVRWp2MVV5ajVdH8Fg3X0lzL9HgmRno49ZsfbwdSoz2cHcLtbYDRlGrLJhrTK1AGAUEbImSsX6+I8YG1jd+nl86lQBFvZ1b9JbUGscvVQNMzTK8kIsc6Cu1azuviDa/iAfcbv0nuKG97XHGEw8iJ27P3gtHCXWchYpTsz6I/i+nCe+T6c+5jg1MBrZM3WmI6FaE0E5gGSSwsyeCF26memo3Zrct0n7zqc5MgzLr7ddRnRFQRlHGVHjVXOrgTw4QbMLOPMVLPGIaI1t5fvBUOpbj4uHApnrxBZ+MT/jPSE4mBZm2vMyA25E9oLwetAkRwP9DPjzqARIGa7/Ll/fMWlT1jB6EQULgs1R3CMBMg3KeXELs4feMZnNqF4sjT1SYeEsrGCioQICr4eU0QDI/iKkdsdqcfw6Xsiu77gGo0fmIaDmAowafeKBtSw5MMSwxk0eInjcXM2z9YBeHMCV1qm16PMogOnLfqPsfViaBUNCenX8aPSGvrP0vJ/iZXsbFunGz6eahERpi7Sli+SFtJOwAjZ9KvMWVp44Vi8nxE8ZE9A6ivBPXiN6J1G8d4l/8AlEr+KZvtnRLW6jrzE8946motdmMdzi3bDAyiwyYEtSsa1YXl9J+dQwc9LZ9Xl41CY0q2tygWCmV9CX4YsMM0w5Pz/Oiw5crLyuVEDBxXm658PtBxeiTNLsiAHSqa7gREocXT+4UclCNJ4lzJvsmwNdk/6BNiaOScw32Rt3XI7Y1aw4Rr6SprRVTCbvqWREttjwdeY9GtWrZ5vmBWa6Q1BbcpbyaitmW4ngz951/K9UKQNIbQuCBbRbE+634gH26wUHw8IU3q+Y0agqhNPou6+kDdIo0bs2i+fiYSZK0K0Xif3xPLbvxL770afzTxyOYwSxeS5w6hAAkZRGtEqlRs3w7fEbHj2u9cfYxLFCsleBUs7IijJBLcm5ZyNRmw0+/8XFAAKVB9zUMOACrctsp5ykNpQLcZKfrDjpq6ryLxw4rxKEOgXAJC7byjAgoobqshy0qUwdmnaG7fUQO7Q5eYndy5SsZ0cpQjQ5tJahvwSwqJNqIrwvQ9y9O6m3/SX1nol3+9alCgv3gxz7z6veApvXcArnfcFbf+j/ELB4zlaa5PXfEePYvS1a2/eA3R2q0Nu4DoqwKNMvRCbV4GvSXRiuWQybEzXuyuhna0LS/TUqgZeFcFerI9C42pDONkU9li48S4sIy/20QcvQ/MXv8A4i+ggjepv+siwF6mRCUJRhoreoo3yU16By+CPcRsjbwr+DEqVUFZv6R2es2SxUEs3NE09R+ZTjcU2FJ/EAQBpNFOeAtzA1GxQb0szE3Prg6rj0gBk4lg7OeT2g2VCiPYuUjqwWgBrpQy+WCaxGpKur3bLGG3aBo07Twe5NS07TiPqIWeYLFUvcS766otPiIzDCcrFvZltfeJtCNCS0y1uIKw2aQ4gLd+0p211EWb31A7e0/edS/4oKiv6oQSbRKD1ILHBvVnpzz7Qill10L0ZTLQGmFF+ImcMq/i+T+Juh8UM01knwCGuyaTfUmYqJxm30Do8GPLK22ANuPibfUmz0/jQhz6z8J8R946jNHVDWA9+D2qEIegtzTZR6M8/SDLZtt5ZeYVZAWnDCeY1u1rB9EZSQVdb4IexrVmn7PiDUa6/wBw8T9PMDWaOPXzE1nPH+4KmGn6zM+LtHL3mKRZQFOiJQh3Fvl+CEDaDraE4LejTH8G4RjDRBSmBkrNx+krS16JamTXULrZvqW2/pP8TU9XJPCKQB1DDQvQgG6RrdQjHKABYOZ4LdQqIAUVtWq+YtZqUHjqi4oAvWLF3WK8QEVaW3dWYrkzP+NKL7UQodMh0TSDR0E+T3XiOECVOCLwMJyq+K3PPUWtfpox6xqKWl6XnWpz1DCrZRwNdRGMG4FsHtD1iWu4FMHtBRhyfmVPXXT3H0u1mNa/o9psWJtkzDRUydyoHAapt9Pr8RI4FWvbiVwqS5WStP1v4lAuvNWLrH5uXMG+5oDXcAOoxZXOZqTXc9YO5cf7tWXO3+oNlfYAnocEGnq5V+SM3GoJWyCVsgluTcs5GopZk3EcifvOo2gnKvTlVjVb8zEIwCtNU0GbfNTWPLaqu0VzLJ946lI2UsQBVGBt9pZJYjwY2JKq9v4z6q/EZ6htJkckUiuEoYkP1szu61zZ16QT2Yk9jHDrTMnWyWtrUz4hdGoXbrcz01L8Gn3/AIqMiAwEF94xVCrZZjLACnYDZZdGI/OlVWabxcE3swBsdmfQmpSOKZ79fO5sHXZOY7ckHa6OTzHZeeSCtsgG1XM6gi0X7kOIdPSpwHB4Ink94A2qI4Yg01ilMXcMABxjcVrUt6iaY4gta5nR/Sf48LbMBU5VHG+JaQoyBtFLECz6QoSlgxS23OC99zOvAFL4UwK/uDXa6jLZQCsLaDAWnUbAxw50PmZwDQFPWV1QK/sSnQgiOEqncq7DRx6x/SxwVasEWTeraFsxt4MdpCYQKobOvTwYgQAgV1KE028QLeDCZGZ5Guo3Zk31Lcj2lPZ7S1MntLVbs/MaHJQInF6PAxywKISu2tGZt2WNZcy0OD2ixhe/Gv7lywBRYZ5lwbkAHqmfaXlkDDW7/wBV7yxgb6mgGup4EyTwcErdUDgC7XgZgkGgS7wfSIUwcwh4ijVu4TUagFaICtEAtwSjgaiFmDcBwJ+86/m7q/Yjt7PvGlsGXuXF4lKGWHFLC027hVFB/U4x8n8THOZM76AbfEssKEHbTh4M+kbFbhwV0dzo7QBL17IQqYAbjr6M2+pNnp/GhDb6z8J8R946gchwxzqb9YpiICIvPTYV7wZWFU92MdUcHriCTe6HQzxFVKsqlfXEWTUXbHpibJxOa5Q9GH5gArLDJb15gJLQ0qvl5+3rMyqYNisFeYjjBfBLj0NlC9D3C7LmFu4iLM8wK3KdwKZ4gK3zENP6s/xqENg2KXAH/ks1BkE0LcZzj+N2MqNumegnUCKTGKjSyFFJfUusuAAxEWdDl/fBP0EwYZ/0KIsqlQBaucURHbk2x9Uc+hjtdTVFAVTe/pUFDrRF3vfpGhIrRbUG0KRWKKRsAoJfR1F1h3Fhwy/DFhhix6j8x11SqGRHFnctIVl3ECrzxS+3UHQti+R2Y0O/dnMtHLOw28vcNN1yx5LTy+Je232wSjZFr0wzv/W/V6IngFbrNODljTJDZVKwQ/aeWoW1vmon8t3mr6wKN7OYXNp5gK/3AVz7wC317lO2u4ize+4H/U/edfyADIUVa5M9WDjzPHjqFb7E9XHjzFlqIhCjudd9EfctXDHy/G4IjW2kR3Tj7+Y9b2jKRW4y5xL2DfDzDWCCdsaOGmWODZAtg1KOiAowQC3BuUcDUJoNPv8AwMKNaixkWs4tC+5S/oLQ92fM+5H5n4m3r/DPgfx8CXKW3mc8xUMQDdXOO34KPMsjFTsHTwHgxNPQ+8HuLeC3c+bPzQ36p8aOyafxo9Jp9Z+l5P8AEANaPkEop3BElCnCVl7V92Ho1Gpa0nhpmidVKdJqfDnwSPwP4n2k+M/wgVQA3M07DXA0e74PrW4KMTK+uvTwfW4xoLM2jDMOawAztwzEWRms5dw0TT6v3m6fhHZ6zZ/Giaeo/i4HDTKlZeFxVP4o3ivET1T13E9K6ivFPXiCiQBytR91cAKfkwN/BZNIaHBKiX5IJugGoWrgjbujrtiNMoGjLZC7moDW2BjbAy5dyuzqI2ZdwNss+O+0RG2jVJUecV7yxcuFAho39WKC3QvHSL6s58z0l7h4rDUr0IwNy158xPgux1LMSMA0P4fMbOGKOoi/GRBtf2Rf+s9ATmO3k5eYLfwOYrXweYm/kcxNscdy3r5gtGPmC5xz3M8OO5fRw+/8IBahyQ3MsF/EEVwBSBa1nmOq5fiWsefwzSGppgeYBhpoi0/TL8R9omwPe5PTXrCeMARPQTHqwsdOWUjHD7xi61s5zmoRHWKuk2TiIWYcwtXEQsxzArUp1ApjiAVrmAKf1Z/iZtRda1zjllbIZVWVW192Cba5gMTR653AgJgMl2/maO1NEcR1jT+JxTZNXal8gtoMq6CACevR9X+vfiEiKauoWsNH5iqIsRMJUO/9LAElFTPCy6fpCgwShrlhXxKcOIizHMBbEp1ApiACnZ+YJYB5VuVa81AgeZEtot5Js3ELe9rjix9OERSA0uO8sIMNnbBcRSALVjAcsWmUbH5fHpDRXAFV/F9ET3yddr7mODUwGtk6rULriF1xC7dTPTUbs1uWtqfH/b/Cn3D7z40sLqAGVmE+xQPO/Ty/MWoCjyVfL59qnGPk/iIoVbQVnuM+ITf1QbehHX0Zt9SbPT+NCG31n4T4j7/wf4SBZN+D7QowgqbKGbOxgL0tttGcsdl08HiFFKC+jpl4bpo+Hf28xRYWAD6Tn6Y8xLhiMdca+DE58ogyyo6Q6PMdl54JQy6cHc3Lp4IGoWyALXmB5IDeTcLudRGzPMRrcp7YDTPEBrfM7P6T/wAA7cyuhT5INdr9bg1Gj9ZiajT9nmHHs/W5eOoRQ+Xl8R2IB27jL4+8BFfo9Yb4v9B5gXdB93mDzz7PWHibPl6w7Jt4gUMntMGzbx5l75PaZ5Guo3jJvqWtk9pns9pamT2lqt2fmVKir2lVGc8I0OfuR7XXc5Dt58xEbVg/UXHpCKnHL2BxLGXfc2Drsnk+82Lrsj5O+49prl5YrT8omjyOY2/h3Ba1Ba1Bbccy3hxFbMcxNsT951/IwlYWuXyYPPY5dw3tAtDQ7XiWh2G3dHod+r9DmPaJFVKr5YO7bt1K8/qMvV06ms4xDtn3Imv7IfoDlCHbby9yl96OZSu9PMC+9nMGW9dyvX3gwb94GXe+5XlruHs058/y2iJigP1zHgptsyrL4giWlw01trEFpnOa8cHPq49Iw0hskp2DPpr1gYQWi1fr/URA0HP+pYBTLb/qDrNHPr4i6znmOphpz59I9Zp5/wBR6JvudQ94ZYG+5fUa7jdmDfctWj3mej3lqYNdwutG+5bL+rP8lbS6LhcEjqDLD6VuEFKkDC8XW+4EW4KcZ6N+M0dotWeOISwSpJ/X6f8AIJcNqWz/AJ+U/wBvL/x3og4+UUjdmTmeX7x6lN3nHuX3DnhCs+ylN7Xh3FNPrhMPp6RtOzpCq/4QKLp6Q7EUu11uLCXFNhSdfSCD7Zi2FYec7hyrKNteSRufbzxy6w+zRUZ9Dj1YaewpKrteWPmtx8S92+s8I+s/5+ePfWfjtgghrAPbH1x4Smdpwnjz0hSQJMRFr29J9E9I29uCq4ASCKCc4jNCmRbqjr/coysuCzMfvEwFBrDRah3MAKAQX37ep+IK5EGAe29vL7cwUAPRH473h3PJ/p1H4epBeOxYDzLq/bRz/bTw3OEq7tNqHcLpwENM4mHU5CAJTK7n/TiBXvxDTf3NHPXcGVYAveY6h3PCAzSyebmWJ5shhq+twZuBs2W4eSLJQEw9Gjl8eYkVdWSU88HgxDhrbH4z/qzAlxHrCg8EnPrH1B5lDoufM8QXmKAx6xuHNzLNMT34iKEa7jfy3uDMn9Z/04kJr7ixp8vMKOLIPk/zFS+VaO0r7M5cLRRLgHDNWuoN0qF3WpYwNc3Bg61eJIV4X8WrJ4D2h0TQS5g9DWAr4gATUT4L/uWNCy1W7Kg0qBTyNnyTGEvtWrJQoarVuo8CCl6rb8sK5GilaoH4lpRog+FH8EwmOrvCV8wO+0RZFGfExHo2rdEwkteRZmeF0OZm0tZejj+j2h2XkKvQfxG5EFPpBBAAHiM7Fr9dn5QRHDbuoiycS3aN/EfZr6A/rLhGwDUxae0HKHQ6Ipq2a6vj4gZDUb4FT7suHXBW7KhQKAfUbJjdZtWrKlYhoLrqV/llr1bfvMRGmlaoH4i5pqg+Lv8AEwALa1vL8onCqrPmGCPLRlt2QXdYNZgKtnA2DzwZh2NcVbsqVGUKeRE+Qh2NXW6snLNAuVDWLXlbflg5dQ1WqA/E4JTJ4u4V/WvW8J+YhFtEBR2VAs9l7oqAgCxeruHb3gK1V/3FBFwPQ1f2IJF1xO6hnFg7GUBQEO2w1dbgCh9QaD8ECWNFa6Vv5hIFFXVlfxzhxoWo3+JsxboFsGxu41WqKiiBcD0NX9iDfV0N4SvmA2FSdxPEPWHat1MSC7yLDybQtUrCyDbbq9w/GHbtdSyEKjsYAAGDEELYJddq/mACGw7aD7BA7c8TzdwiS2HTSfZYOXyFdB/EQoTDDuBgdBDu9jrsa+YDwrF3Wv8AwVdBKCBbkGHELP1qDqaP1xMbYv8AWInXI1+qix0H61P1n9QY7P1xB74Gv1UzjR+uImDZ+uIM8lfqpTfX9cT9/wDiFz9vtDaxr98St1f9+kr3/fpEhv8Av0ha4zTXP2gU3/fpDGy63d/y7mAc8VdlfdEBaY/XEEBpn9cQYun69IMpTH64mpTV/qokhTLX6qJF0/XpP0n9QYNMn61Brww1+qiWJda/0lbqSSQI3wfrU839+kG5/r0nm/v0marx5kpG8Ysqy4makTZyvHncAjWAigC8DeBvMsd4DMOe3wSrD30z/vS6znDlGhve8u4XVrrlG6nG8vEsNM72lCGM7TP+9GiuvtK+tkbeJqcXaNQ+ZECG+qNb99Cv86VhWi7tUL376Z/H2jjVEjl/KKUHAqH6wBUnZnm66mIUyX+qiVYZx+qgkox+uJ4P69IIaZ/XEGvDDX6qZhT9ekSQpn9cQRdN/rUQFp+vSAqTUDG8CKXxFJbX/DFZf8DByYK3lDgFq3b0ghRUAtHj/LBF0LUtIHQKLDSz2bi0S2PSrTSnPcHXp1DK6OI5HA8eksw2dSwDR1K9eyVEbPSHw+hA2xocesHXy4h0Y2OPM6DzxDAw+06Dt4i4n2l9HXUXJh31Fjn2haVlto5g4YddT4aO6jBsATqyGAXFkvhiztznUGWcM503uWLo6mB0dpV5Dy7mS2J8JoeDplnCPbpg3x7GJr8EPRocQaw2cQL42OPWOHDp4mjDvqcW7cS/D7RYYfaDvDvqNgMGgkerIzDhFLawvp0eYxdWjTd2VLP42siCGF4ajf60tX9UzLUdSo3ueu4ehXUaPU9eJY7XiKAurxLf8oa7V4iBbY68S3JqKplC3y8RGnLAa2wGtsBtyym9uoOR1nE4wThsqyzm7gFShsEG6cLet4g3U2rldoijWjy8QFwwe0JYGnaV69yAAlq7QBwye0DbGhymkbduoPDY5dwLY07Qr40OHqBTh28PcBofZhXtzxEY3vqIp37QzgKYC5Zfszbz5fEUqDcYfp+fn0hqVV2j/FwHHQBc83lQkSAiqlM2U/ECs9JoumBvNdwUgFq8EDs8EhdWLiZqCOGL0wiPl0a9uWMMvp1xLDaQryPqgLZNHPrNhOeZUMmznzEczmCUZIitm2I3THzBdXmopZ6xQriFJZBKM8RxkBYOtzMcLoXFQEoow4z8HtKcqWF5MzTsgKusxaW0PzFajoFES8vSmcQ+hWgsBLw7gb6PT/Fjh7zyHvNgNHMqYNuYF8mz8yyuTTEYybiN5tLIAAHg0wTOTcM0ROqWs2us5r6/7cQAcLLGHr2+J8Z9obX16S0y9eviW4MWJA2G7HOuYBz03FfUwbk31LCmugtToIGC2uuaAahkaVYeDyqbyEvL/fKNo8Asuv44/gupr63/AB1UIw7llytOLpv0igtAeYKjLJlwztf6i1vMaZOufr/AyBbBd3f3zCqIV5X95n+hl/CswlQoDXMFaBvmDSeCBvBRcBT+/wCTb0PzPk/ifePvNnpNvofaaPq/eH2iq1yQybsD8hE9eoco1N1IB3LrlBV+t6viVWfJ+5D9ITQAOp8x/ilULFLzXrAjRVdtXJW35hahzV8Pp5hPOV7LBH5gazXUwKJadJUbU6sBwu11CnuH0AcesSH4Q0dJbsjbG994aC+ZQmQnEEFjBo49ZoJzxKhhs48wHE5nJdadQXRLoDZeSOKy1sMHiWG0aXWQiFmOYCtE6Mx2vQcwEAeo9g4lQKxZy+pgsIWjzH0iC7OAPWVHmCmXM+B7Qz2GLWg/iI2YAhQH0a+YP9x1hQIwYEaBvgHHr/Fjh7Ro09pqDRxKGDbiWOGzj1iFcGmAxg3LUQ0MsPjh7Kg7mhnKGf6gCAs2vklFQ2GH0/VesICxdm36uZUL24yz4T7fzZXQwr2ctYi3DfcWOkaWWXMHLUO66BL3ZNYBsqurlh0W4vqqEGA9d0gH+HX8Gpr63+MErRaFrKAEZ9AeHzAVBKRCqOpRFojVTTefMIQA2WHpdEqbeFkHm+4CEAYDUFEOHXr/ABoUNi+olK0lkDTV0nowlAEAGspWZcTS4+Vw3qP9L9KZWdW/WbqKgKsnpBKC0Pc5lCUgwtHEces8kuvE9SjjzLLq6mpNHHiUHDbx5ibUNKlequ5n4gFVHfZArwrKug8y81JDQfPgRbeWxsP5mLxtoHtgM4NwBQG3+NhYEF4vJWff0gw4YRg9OE9I3uzJYDQvm36RGxWGNNoIQd+ncHI6qLROxYqry1Ha9H8GNVPRh3l9SBt0HPrB9c5h0Z2OfM7LzzLpJRBlVz8R5jSlqgXVuozkCN9GUfsW5srviA5Jzpeh/wAeZ8QLfQ0HpAwy6nw0SW9hqiVDTmGlgCrb05e8JJphkM6xoLeHmGQe47L8sGw6l6+IqfJqDf7kTX5J6YHMGnsczL7Dn1mnLp5mjLvuJ2CyqNN9MUgVV0tYiVQ772+jyx+BBsT3u39qHIvGuI7G0sF16QY277nxn2lRscIG1h+jsjvjFW3sAdb+UVih1m9ix793iOAUW0WmfmKh3F8REe56g7Gr0i6GweoormfEYboPEt/ymOBK2l8XnUcAJKei0XUthbUsLTaxsxuDuoGuXH2iS5Hab/EBt+Uz9X+oHJ0D8ETGqp5J6S5JCgdEqxOv3nEsI0AyJ5Y4PeIhEElRVLa41NVRyduUvE7GKnyajpPR/Dpe4aLqmsS1uo68xPJeOo3qjruJbq8QN8tH2g2Xu3ruJmOBwwdy5TkIWr0dsCddsCnHm+P+y1u0z3leWDDLAy5dwcnL/EwKVMqNPnxfpcUdFKKGlDHHp/AewkslU3yRVhRbbYFYKhQCofIzPsS07QcdwLCwOaZ8xpLIAcqbN8Su/sg3NOob5fQgXZ0OPWDf5cTGdhx5nYOeIxG/Tqdg28QMMSbKNsJ15jjDPLxGVsJm4M/R5jyyJvh4meyGDJqWz9P5Nh1hazN7biqmbBByKH7PeHsVRFourIkKAWqnmFVAHNuqqIS4qTJuDd268MF+fZE7+yHc0OIa5bOIL87HHrHDk08TRk31DtYJpuDYEsHWuhz6uIbGFtUtPLAlhXtM21qN2alq2bnxn2jacVpLp8QEKBTZjURqbWtbgzduc+Hpbb94RTj9tZ8O6TxnuNqloJS+S3TULzQaCGLhna1s9owEpLDmsx5jTw+Je230zlGumVA1CyscsWwNuGh4hBDLcILRqU9vUPEWwK1+kVcxAHLl4+kFFEcaL7Vy/MfgUAwANkoxHoQFvr1LPZ1EfS/ywhVcFRflmxkbWgNoURDbBbmo4pbiLcWNcRaGnc9Hu/1Fqadx3w+pBsxoc+sWht34i4tjnzFdjTuIImAO/EzRjB9dxYR8KYMKvBM+iE446HD5YUAOkqsU0g1Tueg95gYIbYN9y2Stv8UWtLWwDDW020eizbAoKZyvDVymMVs5YoPSrp1AVrXlABq2y8dEPGQwAeK/MHU2JQim6v1iEGLVC1WepYLgAaYA58KWe1lBaVvct/wngF4jf0kFbqOTzE9e5JUO45O4BFdoLCUgANqrEv4tQ3knj4mcnjmL5D7HvCtpLqgVfUzhV1L6kTWmW9MFoy1OnWEoC/0MUkM4hbSkXK8sOcXozmHpZQr0GllK8g2W15bpzkPaM7EMrpZzfQe04BICpWoOuzqDTFhpyZxhV/ARzOHeC3l+rmN5bx0xL/iiq3+Imzo6jCXZdRNdBS63XvLa5aZn4cYotzAnGt4/F9/bxFfVOS4TRhjlIOrbl5YdS2zDE1p3PjPtBtFtRDJV+f8AUoEW0xwK46A3AaAdkkMAS6zGrWhQRuq/3EmuQNlc8QpsTKsGa8dSu9ZadtfEdQ08+kvYN9wiAwn4jba5BNI0M0hruE6VGphWOO4AB0Wgh1XH19oWTIs95vXiEEvRl8JTAgL3PSYjgCDhglbIJbk3LORqI3GsdQVxyDdLJvN3LpVTRsbzQuMbl0CEFraoBW+65WePLMOqF3t3V8eOIYSyFkJd4Cs2e0EeMi4AKD4XzB0adod4fUQNsaHLzB1bduoPDY5dwbY07QVBqjjeIhBWWC6HQ5+BM9UZatebXLM23PEs+UFDeO4ssPtL8PtFhh9oO8O+ovcf4sQRcwNU6XHMONHOwvxiBaDuLCAF4LRmjtQcCIb7KgAal9bDWuZdFBnC7GyvSvrBaIsAbcjea8zwoHQ6qq+ZxSC1ho/MBABKvRBJxT0u4nDK7NxSIVWh+Fr1YJmk2lr5Yj07hiU5cRSzJuJZsAq8SncBTJqK6+kFoCeYqiixvXMabZhktkIRkZWnOhg/X3fiWKCKiw93N/g4TWDx4febAMoFgOrmut/iW+EAgpLCrOnma40QA5nRo7eLidjqXgMlvEsC05wo8H7srqhg0yHT5jIw4MacvUs7g+6DHrBLc8yy2eIizJEVs3PjPtA4uKFRddMB7ujFnNOvMGwDhINoN4ji6DgN6/3LrUDPdq11FwDdxGc4ltz52RDRw/iEvDmE1mmC+DMAjc74Fowb2L7fWciOS6aIhYiEsKL7qUwm7pVwF7Oo6YalndblUw0VusZn4T4X+OUWK2UbPmMCJuiiIUE2/dHZWHc5xXzFzEgW2M017Qrbpbf0OOs+8IjaFD0H9r8Rmw1GXmQJbJo/MRvN/iI5Gz7wHllKw8uoCVTpMpK55Dxt5qZkzC1zV69JZCtXUXLrfIvXrFMesRaYlKQrPrKfUf4o2UpLhttl5+kA5QGxsByLl7zDPJi7VpDm4TgpApHwnw42yw9ymNYJum5nTV3njvz4uZqV7K1z/BNgNZIWuhDVXJPMPgPzK3EhaopbuHWsYVel39IEoiuL0HENEvxYFdLwPX8ZQy6js9Zp/Bo9J8NGCxVCFcW49CLUwohAxleDJP0nc2wPEAGsHHP7+JTTXeV3MmC5q1ko2+8wn/NeJ+14YxIRrfj+ENCYCLiLqmanb0eWZEDUot7yvX2gfK7rsZ5izEhHPbBJUDgtg2E5LmhDn1nL0jsmn1J8J9po8qXf7ZC6tCc/uH2ivMfNGPY08M1TglVwbfvD6MiIxw/iHDpfR76huTWAgOr5fQ+sQKZFrB8HHruLyrSBavAv1hxm8OLUWwy1ZgRhY3HLay5x1EU4gK1ArUBbiU9kDVx/jRJkckGBcNxBgEClV5SkoWKscx0MBQXJzG3WS709Jb7MYu8f3NAa6lwgYOL1AIAkKdSyGwyH1hehUpMsZVqNQ5j2+fbuA2bBQKx0w28pnyfaAoHtCXgc8RDGDcBbBKOiApggGcG4D6j/ABstGGzl52ErqBcYQU7cucq53uUhiB4wdL3bcOiWhu2Fty23zNL47mldcx1XDz6S9hz3Na4ni+ZpHHcAWUclie7FAefPX38R5bolZBgmxJjsaxA+cwEarKT0ggVQVZn1YrECNvHhGA2jOZQoQFSiBSD2UaDY0tFJTiyPxR1YwCrzxT7+Imkblt91l7jjuUuOO5xnLz5ml8nPiYmH6pqLvvwxTIwu2WNZgOCo4IklpEvMvfsRBlRbaG1dspoC13ObYiuOGWh2OZaKUnJKQFEBn1lFvSIWQFfWfGfaKFlN5sXdnfMNEDgcTI4Zo1ORh3ax/tkf+yemBzEFZBoNrnqLIXirI9n9x6gbGlo3x8LGH1E0P05+t/SAVKY7nr902BhVyu1hEcjqCsgAAOInI88zNy8cxMOX3gY2+8GNvvAy5feV2ddwczrzHUOUTbQg1eOK+viOrseEXVEwF/MoK2lLXLlj6SefSZe13PRLufuZ6JdzP8pN3Uc+sv4ukHH1Q80xaus/uPzmXr5LhQ8Q1BYMA457mBy28+Ych7zNk88xNZdwZZZXlgwywN5dwVkd/wDHRWSFFFtv1iigbVFVlesfT+D1FbYDinGYNvrqGy8cEdl08ekNsv0E3DrojYK0O0IeGswLL6HXlo8zJjqhZ9/6o8sXp4NGnesEG354IoFcFBp7ih1OQYgC2CFlNjYlTwoAGpTy4iNmeYGt/Ep7gNM8Tt8P5MhLwLlznoL0QUZ66J9YvHmeo/R1NjBfKVzGQoQ4NG64lyxLAZf6PLiEEcw19e/3BDaFnAARHjuAKDumC3yOPWI18HiOEBKa4fMD6Z4lPcDRmA5zzKbZ4iNmYGt8k+E+38obU5pReEaiAHKSm1fKvkfxBQlRo099/aByrtmjPLj658MWcfx+hd/A8Sy8CAKBqG3l6g9A6mVEhoLdQegdQ4eTj0lW+HUIsxng7YW+euIjTmA1uA1uA25lPLiB3cf58QfBfmd023V7tjixo/iZ/JMseH8XGaS4JHKqCZ7dVuT9bi1AUd2EuY3kfeAyZLXY0xhGsA/EqzyNWU7h0Jm05jeNbiKarIhmr5fM/TPzE4vNWF/Moax0KjbXhlslbf4peP01OcKesqOysaFVo8GogcFpbKNM6qjxDAVV35xtiLXa7g6ziF1mn7I+JuKmNKtQvoxl9I27sz9gOPqt8Qul5Sm3tvK+WElU65VfL4iqqyKJbsrMWu5/WI6mGz5TonPMMDB7wOhlrOc6ltXzM8OO43ZjnuWrXzLevmWpjjuWz9ILd9yl3atZ3+I1CLBQrleNQhILGVnxKi1mv1qKrHX61HxNvy9Itds+HpN4RLNAeWpWuT6hXN92OrgqqbUnPduV8/aX1/r0gxDWjoT0agqOxAwriK+xs59YtOGnmaMN9x6DTzM9HvC6MHvC8457mbYNdxWzB7xNaNnM+M+0QaJsZ6LG68d43EmpcLFrKs/Movc+6V6uYbVMzbdfgm5iWEq/Try0eYEynYv6/gV6sOoNaCjcPbI/QH8TP5J9CTfFDQsn0RNd8n4m3Wo9DmZuuI3TqGHEMOIZOtz0NS3FrHUMt44Ka4t83BsHpksLt8y1q7VpPLH0A8ekydjqMd2rtKspn6CXsQZU/bixvMFrzdDxFAEmbQ4rmaKVbSlarifUBx5mV0dTJw6PtHhw7ePMHgfaPODzF1h3LQNADVZ9X8A3VOoaM+Zgctfki4Hb/OoZDE8q09QdenaB1cOUGoaG2kNNspJosJt6N/b5mdoQHsKwPB7yt+plRZnEHRp2h8PqIG2NDl5g6+XKPgEK7WKb6gcXnhj4YUO7p34gVzt4YHN7SnbXUXJvfUCufaU8+0BRv2i9tKlRmSMW9OdXTBvjTtBpjTtMciV0FbmZmiCbHa8H7mHWpo3d18zjxMCNbINuOYF8e5LCwmTLDhmCEwpyQCIxlsBBXjY5eYiu9PDAxvfTA5NPDKefaIo37QTO99Sy29dRSzftEVzvqfGfaV/Bv2N28QZg9OC0R/p9FvmKqNyt2rteWbh0ckuM9vJ3DZeOSOw6eTxLmXfZEhXSjZM4TVkctzYOuyYGe3J4lt8tdxXY55jb5ccxWmBa1Ba1BbcS3hxE6uP8c9EVS1NXG0B2mD1YtBhVu/KOsaefSW8G+5pDXcE8hZ/pPLN4EC+12+ZfwfVC9ho59Zpzfc4Rs58y2w13LHDRz4lBybefMNge8K8jnmKYybgc21s1kJ5/jJgqDnSjwQXFqpbzlEbOX+QItBbMl9BV2KSnxBLGtrHLh6SCNo1AtDwG+3em/EDghRho/wBj6CKiNpylrGbuJZ3EbDUZyIE2Gj8xG3uMoybPvEfOCUZiK3yxS9XEdnrFiXDT0nw0OsftU3gt3LALrThy2+fvBasspvmXAArli8wuFqx+PrEirnDvLxd+r9BjgCwQttW1+P8AkKJQDAIzab/DEXyRStkZsNERWRtEXzyfmKV9GIx6xc/L+NEHfrOXpHZFj6k+M+0YGckLcgozj/kNgTcp7B+9wgXZ+AsmumObEC3t9IgG/wBHxDLQRKUr0ilKAs2ijxHBUCzTeuIJVsBlQq+PJHcKReV61FCQVaQ9oD9H7Rv8LEpfiFYsspgaPEFLVCmalfHiGV20c1fSYyrAvctHEpnAquI9oAICWP8AqjyWEs4muo1iXQMTQ9eZfuCKr3qJziKC3fXQziDo+hO4OWaP3iBQBt48oho4ePSEvA3EMWifP2+Pepn1/TzR4IWg1CXiQBbBo/MBrN/iBwNn3gLYNdQF8cH2gKccsA0Ql4dxNY5hC6WLM0HJ4n7F+JmAppcPxGKS7CjNeDzPkP8AIkRYlMMhJ3V0EDXF3e4OKQr5bK6xgNR/haXaUb6qUhqYAr1mIqiJ7XZ4ixmWNY3W4PU2PS+8u1Z+vMAulKvfmY9Cvgjz3cDQyAYZV535+JeBXY4xz3DSpsGcG63zHsi81rHPcKqSPJ95fH2tHt1v6QTMk6tHnu56EGvd73Eqi5zfUd1H6GzJ8+stAV1j/vLbZPn1nmGlRVaecGXiEc1uHeSkzNC5Ss49NY9csLWhbdU2K4GLAggHV3uWOaVnLTncABkOXptXPVQ6oxFxq7jqlQ+Ueerg+S4GNSVvzf0g27r6n1lwhd7PzAb4TNXe4cWl/NY56lpiAFWVed+fiPW1HzVOYGVYFrOdbjmhB4rHPcoAq6z/AN4OQFLNfmctQ8U893DJJgMURc78/Eaq0foeOe4WEbKPgRTfOvrCMqBRGl7aF0ynoC1RZ+2ykrYqbAH6lwfUVMWND5ORl8G6CFiQH1iLF1icBXN6TNReMKrS/SUAacF0FvPrKnUso4ZVUM1lWC7OymCwB1dZXhYrIKgUlAvPbxLIRLaoasHm6gLi2jlS5rkZhkezW7zcoXo1ZdWRnurWqabHD2cwsNIA2KfRpgd4S1Ksgc+eIhuDa6Al5b2OiUlo2te8WTtrUuZr1PaYRGaplafrsjqA7DbW0NX6VC9gWJbLXrqUd1w9Gee4sZgNmtU3fh945DlTwz59JpixjC9Ln6n+Zqlo8nvH0NbQqKMbnklHYtz1UcAo0249ZUXs/opfPVxIYEG2n3g+DoUNvvHVn6sHbqoVgqwWmeYNX5TsU56uKxgtZrOtwLIsrye8/efzBUIoCt9Ljap6ooee7l2sVGBzbl6r2/8ADFIqistexTni4ZHyKs1VvFles0MJlWaxmFBhi2AWwtZRa9YTNWUAovGDA1V1GOBupu0cALFVdNayFXmi/eMrgIDWQMRtAsM2y3k1hPMxWFxo5hRxEaAFbXlQq+VlwJwOT7cv1mA+4Arp2vBn6sMIvti8UIymHDN8jVCzunDKOGijYUsvGS4CEVte8oE2gBG1811nlZXRMaE0MoYu7lhtqYpYLVZ2VDNut0b1Nu+PH+FetKesB75o9o1MS3rFfqbM8wLZavrOleGnMSlNqwBW3secbxCRaSsNS0zgOmjTecZ+kbYrQuCqH0KIp5Apu0XhepkImCJJcmzPMdJYiQaVsO+uoJUw3WVq3yV61OzzZKXwRm39oDVQXOPfMBkLwFC2sGBqoDDKQY1im7W8U+sqaObWi+0v0i2AAIJVumPmYoJsmbzxve43fC6xcqusbR5CqukJcafRRo4NZt+v8gRaSmGZG7C6iBri7uVlYDgW0HHgJjKNhCkrXv8AaPew4IMpS4PiA2QZV5l9zcgaEp98e0JehA8ELsB4B4FfEEJQigGgNHpEqw1qJffrxA6nGBQlD7ceJR4Xbgc1DFEgCAq+K/alzIhVvWIZ9h9QtHGevEUW4gaFWUY9WCUBcAN039HEQ7AwFoqZq+fj1ha2dAasgRMGWwlbPqkVppartVtfd/kLFQVtbl1ndS09lgyOaePncHgX6qzMECKIvO08eTGoRLAMMLKJTNxSFECmu0etSwi2yQDePF+7UzmLC1T9Ilp0ErWRrLkzmJtenJdq1ErlWRI1PQBxXFsoCK8ubti/prEKbYtWDTLyerlS6auDYNtecmaO4grMtZe30qUJXhGtV0OTettNQgiXAveTK/FQnR9wHBScPoZ3CVtkvDQW0lcnURZS8QU683UYqhRRS0q6xXnW4a/9bgFJsDK2AH1ZejIDkSs7p2ainCkG63BXRaowF5eSx9o0gVKsspbu+hixgwHI95o+sFCzQrJVPclU7bgQVlAe6x5jiPGATZYLgsMxpVQgAgi7c6QagqqpYPNysMksKE03d03/AOtbI9Qq6FznXHUMAPRUSF7s2bgcUbodgr5IpGKdCjbZnxrcGePlUtjt8nrG8MSqrKObYizigoVAyuIFCJaHq8uGEShwKBpVpzz/AHKGm9OjICrOMpuLNXYq2F4PQ19IdswNRatOPS/r/wCjqAyIwjsq95czNvYiuwr6MQDiKurCCW9W30ZSAi6Ia4J8mNekdly4gyea1VYx9dSjxAqoGqeW6jkIyMdwjosf9RhxVoLZKGmjXDAkDrQ8yvorMLndRqwFVVh3LGtcQBtm67h+BXIpOx9b/wDRMfSheBaWUgMLOpbRDrDLCUrLEfZjIcBDbN49cMQVjE0mjWvclMm3JKq7+03wyxaqlv2H2gawsM8FBfcxYPhVtv3RHIFZNbgFfR7y7C90IWa0HTMfPVqZvu8BUIsqmnVn/wCEGgaSheGDBUQZm95c363M+8hfIM8ex9IOkqV6O+85lx130pK9KWCvIhux5mVZfyXfnOYVQVVVaOjolOPh09OpXRXatcIFewe0W3WQBKvdvP7uLoq1Ge8Hpea/9Rr4but5vPecxCI1HZBSzuuI9jUsy1q79FlnNrrON66MuPMfDWw4v9CJx4izXD1mOg8iJYlXVPG33mgOlUFVr+j2lGDeq8fhiOU2aU0tFx5SXQWsLHgfWYvCFXgC8HRa+/8A6oJSYlJegBt9ICyDY1llSiCoBbbRzK/ipzRKhZAAoKCV/wChkwCj2SkGoLcgGhCJtlKNKFZXn5lXNqFaLr6QF5D264/pGrmzQ1v4zCiW4+MEq7uqXHmBCWvmltlXuoeVAotq27a+rPILLd2vw9oRDQJkjZmnPZMxMC1VZXymlkD0P/rP/9k=)
Massa da citação em relação aos anos desde a primeira publicação. Relação entre a massa de citações de um pesquisador e loge anos desde a primeira publicação revisada por pares para oito disciplinas Compreendendo 60 pesquisadores em três diferentes esta¡gios de carreira: pesquisador em inicio de carreira (ECR), pesquisador em meio de carreira (MCR) e pesquisador em final de carreira (LCR). As linhas ajustadas correspondem a amostra inteira (preto sãolido), apenas mulheres (preto tracejada) e apenas homens (vermelho tracejada). Crédito: Professor Corey Bradshaw na Flinders University, PLOS ONE .
h -index, g -index, i 10 index, m -quotient, Journal Impact Factor, Altmetrics ... hámuito tempo éuma questãointrata¡vel que assola a comunidade de pesquisa - como avaliar os manãritos relativos da pesquisa objetivamente entre as disciplinas e fazer comparações justas entre as primeiras carreira e pesquisadores estabelecidos, diferentes gêneros e atémesmo diferentes disciplinas de pesquisa.
Agora, o ecologista da Flinders University, Professor Corey Bradshaw, e colegas desenvolveram uma ferramenta para avaliar o desempenho da pesquisa de forma mais justa; um que nivelara¡ o campo de jogo não apenas entre as disciplinas, mas também entre gêneros e carreiras, eliminando as dificuldades de interrupções, como a licena§a-maternidade.
Chamado de andice Epsilon , em homenagem a letra grega ε usada para simbolizar resíduos nas estatasticas, ele leva em consideração as muitas diferenças no espaço de pesquisa para fornecer uma comparação mais justa.
Um especialista em estatastica confesso, o professor Bradshaw publicou o funcionamento do andice ε na revista de acesso aberto PLoS One .
“Um dos desafios duradouros para os pesquisadores éa busca para medir a quantidade e a qualidade de sua produção de forma justaâ€, diz o professor Bradshaw.
"Nãoháum processo direto para comparar os pontos fortes relativos dos pesquisadores em disciplinas distintas - alguns apenas tendem a ter menos citações do que outros.
'Então hágaªnero. As publicações de pesquisas para mulheres podem diminuir durante a licena§a maternidade, por exemplo, afetando o desempenho percebido, embora sua habilidade - e sua pesquisa - não seja menos brilhante. As mulheres também tendem a não ter as mesmas oportunidades que os homens ainda hoje, por isso são injustamente classificadas em relação aos homens na maioria dos indicadores existentes.
Painel esquerdo: Distribuição dos resíduos dentro da disciplina da relação entre Arel e anos
de publicação por disciplina (ARC = arqueologia, CHM = química, ECO = ecologia, EVO =
evolução e desenvolvimento, GEO = geologia, MIC = microbiologia, OPH = oftalmologia ,
PAL = paleontologia), cada um compreendendo 60 pesquisadores (30 ♀, 30 ♂). Painel direito: Distribuição dos resíduos entre as disciplinas. Crédito: Professor
Corey Bradshaw na Flinders University.
'Onde os pesquisadores estãoem suas carreiras, sejam eles empolgantes recanãm-chegados ou professores ilustres, também pode ter um forte efeito nas manãtricas atuais.
“O andice Epsilon éuma nova maneira de reduzir vieses sistemicos na avaliação da qualidade do pesquisador por meio de citações, fornecendo correções de esta¡gio de carreira, gaªnero e oportunidade para manãtricas de desempenho baseadas em citaçõesâ€, diz ele.
A ferramenta estãodisponavel gratuitamente como um aplicativo pronto - basta inserir alguns dados para uma amostra de pesquisadores de bancos de dados de ca³digo aberto como o Google Scholar e ela faz o trabalho pesado para produzir o resultado, permitindo a comparação de pesquisadores em qualquer esta¡gio de sua carreira e de qualquer disciplina na mesma escala.
a‰ uma benção para quem deseja usar uma manãtrica objetiva para classificar pesquisadores, seja para pedidos de bolsas, entrevistas de emprego, promoções e prêmios, ou mesmo como um indicador de desempenho da equipe.
Relação entre a massa de citações em escala e loge anos de publicação (para 480
pesquisadores em oito disciplinas diferentes (ARC = arqueologia, CHM = química, ECO =
ecologia, EVO = evolução e desenvolvimento, GEO = geologia, MIC = microbiologia, OPH =
oftalmologia, PAL = paleontologia) composta por 60 pesquisadores cada. Crédito:
Professor Corey Bradshaw na Flinders University.
A abordagem para desenvolver e testar o andice ε foi em si um exercacio multidisciplinar e especificamente projetado para garantir o equilabrio de gaªnero.
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A ferramenta foi testada e refinada por meio da colaboração em dados de amostra com uma arquea³loga - Professora Assistente Dra. Stefani Crabtree (Utah State University / Instituto de Santa Fanã), uma gea³loga e paleonta³loga de vertebrados - Professora Kate Trinajstic (Curtin University), um quamico - Professor Justin Chalker, um microbiologista - Professor Bart Eijkelkamp, ​​um paleonta³logo - Professor John Long, um oftalmologista - Professora Justine Smith, e uma bia³loga evoluciona¡ria - Professora Vera Weisbecker (todos da Flinders University).
A amostra compreendeu 480 pesquisadores com perfis do Google Scholar, estratificados uniformemente em oito disciplinas (arqueologia, química, ecologia, evolução e desenvolvimento, geologia, microbiologia, oftalmologia, paleontologia), três esta¡gios de carreira (inacio, meio, fim de carreira), e dois gêneros.
A professora Justine Smith, que tem sido uma 'superestrela do STEM' promovendo ativamente o envolvimento das mulheres na ciaªncia, tecnologia, engenharia e matemática, espera que o andice ε faz a diferença em como as mulheres na ciência são vistas e, por sua vez, incentive mais mulheres a entrar As ciências.
'Avaliar com mais precisão a contribuição das mulheres para as ciências e celebrar seus sucessos éfundamental para encorajar as futuras gerações de meninas nas ciências; como diz o ditado, "vocênão pode ser o que não pode ver". O andice ε da¡ uma visibilidade mais justa e maior a s conquistas das mulheres, e o fato de fazaª-lo em uma variedade de disciplinas o torna especialmente benanãfico ', diz o professor Smith.
Como funciona
O novo andice éum algoritmo de classificação que pode ser padronizado entre disciplinas, pode ser corrigido para interrupções na carreira e fornece um limite especafico de amostra que pode determinar se o desempenho individual émaior ou menor do que o esperado em relação aos outros pesquisadores em uma amostra.
Usando o ca³digo R ou o aplicativo online , são necessa¡rios apenas quatro itens de informações de bancos de dados paºblicos, como Google Scholar ou Scopus, para calcular o andice ε de um pesquisador :
o número de citações adquiridas para o artigo mais citado do pesquisador
o andice i 10 (número de artigos com pelo menos 10 citações)
o h -index, e
o ano em que o primeiro artigo revisado por pares do pesquisador foi publicado.
A ferramenta também fornece um manãtodo simples para dimensionar o andice entre disciplinas com tendaªncias de citação varia¡veis ​​( ε ′ -index) para permitir uma comparação mais justa de pesquisadores em diferentes áreas.
O andice ε permite facilmente o benchmarking de subconjuntos de pesquisadores em somente mulheres ou somente homens para ajustar o limiar de forma que as classificações sejam mais compara¡veis ​​entre esses dois gêneros. Alternativamente, dividindo os sexos e de aferição los separadamente seguido por uma re-combinado classificação remove eficazmente o enviesamento de ganãnero no ε - andice , o que édifacil ou impossível de fazer com outras manãtricas de classificação.
“Nenhuma manãtrica de classificação éperfeita, mas o andice ε éum grande salto para superar o preconceito e fornecer uma imagem mais clara e justa do desempenho da pesquisa em todos os setoresâ€, diz o professor Bradshaw.