Overview - a framework for reproductive ethics

... hadreasonable answers to these central questions, then we would have what I amcalling a framework for dealing with these issues. A framework is just a starting place. For any particular case ... variation among cases. For a given type of ethical conXict,there usually are a number of morally relevant ways in which it can vary fromone case to the next, and these variations can make a ... rather than deciding on a case-by-case basis. The third approach is not amenable to this type of broadcritical assessment of an issue (Arras, 1991).2 9Overview The fourth approach is preferable...

... without permission. A framework for analysing manufacturing flexibilityAdegoke OkeInternational Journal of Operations & Production Management; 2005; 25, 9/10; ABI/INFORM Globalpg. 973Reproduced...

... successful exchange of IK. Application: Transfer of the Washambaa agricultural system to Rwanda adaptation and re-transfer. The Washambaa of the Usambara Mountains in Tanzania had developed a land use ... considerable (academic and descriptive)information is available on web-sites§ use-lists are in operation§ some databases on traditional ecologicalknowledge§ mass communication coverage related ... with CIRAN (Centre for International Research and Advisory Networks) atNuffic (Netherlands Organization for International Cooperation in Higher Education). CIRAN acts a hub for a global network...

... termrepresentation while maintaining the idea of a stage of higher-level visual processing. Abbreviations— V & G: Vartanian and Goel (2004); K & Z: Kawabata and Zeki (2004); C-C et al: Cela-Conde ... task that participantswere asked to perform. Kawabata and Zeki (2004) asked their participants torate the beauty of the stimuli on a 3-point scale (beautiful, neutral, ugly), whereasCela-Conde ... Kawabata and Zeki (2004) found activity associatedmostly with beautiful images, was most active after a reward and decreased itsactivity after punishment, whereas the activity in the lateral...

... Adequate mechanical translation can be based only on adequate structural descrip- tions of the languages involved and on an adequate statement of equivalences. Translation is conceived of as a ... M.I.T. approach to mechani- cal translation is aimed at providing routines intrinsically capable of producing correct and accurate translation. We are attempting to go beyond simple word -for- word ... perhaps the most formidable task that language scholars have ever faced. Two Approaches Attempts to learn how to utilize the above- mentioned clues have followed two separate ap- proaches....

... interpreted as a metagrammar, i.e a grammar for generating the actual CF-PS grammar. A GPSG can be defined as a two-leveJ grammar containing a metagrammar and an object grammar. The object grammar combines ... German belongs to the class of natural-language phenomena that require a closer interaction of syntax and pragmatics than is usually accounted for in formal linguistic frameworks. Computational ... Generalized Phrase Structure Grammar offers tools for dealing with-word order variation. Some slight modifications to the framework al- low for an analysis of the German data that incorporates just...

... src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDUAAAXeCAIAAADw5oNDAAAACXBIWXMAABYlAAAWJQFJUiTwAAAgAElEQVR42uzdu1Oc63 3A8 YddQCBNJgWoScGWEnGTGbtaLvWZyeD1Uecz0DBnzvkLAoVqFeAqpTy2UsA4nc7s0LjmVjlNCiMVKZYijVCVQSDQsil+4tGrd2ElHSEuOp/PZOJlry9rFXz93PrGfv80AQAAn+7vv/rel3CxKr4CAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAAAAfQIAAOgTAAAAfQIAAOgTAAAAfQIAAOgTAAAAfQIAAOgTAACAz9DvKwAAgK9VdbxRGb3Xbm2e7G7rEwAA4MpUxupDD56klFJa3H80cjOu2X9tAADwVarWJvPtgakFfQIAAKBPAACAgnZrU58AAADXgvXxAADAFYilJsUBk6P1pcpYPZajHG8s6xMAAOCLN8ng9GK+52ClPKErHo3/f7S+dD03HdYnAABwU8WoSE6Oo/WlruGRt8VysrudtxgemFqo1ibyq67ViIo+AQCAa10gg9OLh6uNM++v1ibbrc2DlZmPHwmpjNWPN5aPN96GyuD04uD04nmVMjTbbLe2LjNgrI8HAIBrWiZDs83hubWj9aXSQwNTC8Nzaymlg5WZw9XGeXHSfX9ehRKON5b3H40crMxUaxN3Hr6sjNVLzz9cbQxOLw7NNrsf+kKMnwAAwHWMkyiQo/WlYmbkYZNPGjNJp3O62q2tMzPmcLURzdNubZbGag5WZobn1obnPvkTf+Yv7r97AAC4nnHSbm0W51bl+/cfjfRIhYGphaHZZveB8bFSZXB68c7Dl92PxlhKSqk0WnKyu32wMpNSGp5bu4RRFH0CAADXNE6KQxkx1ysmdPXIkjsPX8YAS+9PiVlb3fcfrjbara3hubW+0XtXkij6BAAArlh1/F1y3PrmDymlo/WlYofc+vbPfbdHXz2udw+bVMcbtx486c6Sk73nKaVOpxM/xvDI4dP5Nztvs6Ramxz+YSs6pG/0Xr6G443lV4/rt/7130uJ8upx/eTFs1vf/CHur443ik/QJwAA8DWojNUHfj0ft4dmm5W79w9WZorTugamFvr/+XcHf5zo7D0vvfDOw5ed/Rf9441 2a7 O4jP5gZaa900wp9fX1FV/S3mm+fjofgyEppcrd+7e+WU4p3f5xuzJ6L4+NdPaed/7vf2//uF0cLensPT/440Tn1V68pDJ6L27oEwAA+HoMz63FsvXYXCuWoQ9MLUQbxIL4V4/rpRUjsTXw/qORGDPJp6C0W5u9V6eklE52t/Pp8vHyo/WlwenFmFcWmXS0vhQr40sTumJUJzYprtYmu9ex6BMAALipSn/9R1oMzTartYmT3e1Yi3K0vtTZe368sZxjIJaORCoUz4yPJSsDUwsDUwt3Hr4sfVBunpRScSOvYmPEpsbxiZEx+f3zmvvixLPip+sTAAC42d5bMbK7HVO2qrXJmKw1OL1Y3MIrEiX6Ie4spkW8JNIlb8ZVfPN 2a7 Nam4zNu/L4SUkM5uThl0iR+JQYY4ndveIJ8YkXu2Le+ScAAHDFIjaGZpuRK3mKV7U2WcqMOMOke/pWu7VZrU2ct7VXTpST3e3jjeWYM/b +a9 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 6A6 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 3a7 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 0a+ QzH6nhj6MGT7ilVxZKJl8dxiu/+3O 8a3 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 ... src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDUAAAXeCAIAAADw5oNDAAAACXBIWXMAABYlAAAWJQFJUiTwAAAgAElEQVR42uzdu1Oc63 3A8 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 2a7 O4jP5gZaa900wp9fX1FV/S3mm+fjofgyEppcrd+7e+WU4p3f5xuzJ6L4+NdPaed/7vf2//uF0cLensPT/440Tn1V68pDJ6L27oEwAA+HoMz63FsvXYXCuWoQ9MLUQbxIL4V4/rpRUjsTXw/qORGDPJp6C0W5u9V6eklE52t/Pp8vHyo/WlwenFmFcWmXS0vhQr40sTumJUJzYprtYmu9ex6BMAALipSn/9R1oMzTartYmT3e1Yi3K0vtTZe368sZxjIJaORCoUz4yPJSsDUwsDUwt3Hr4sfVBunpRScSOvYmPEpsbxiZEx+f3zmvvixLPip+sTAAC42d5bMbK7HVO2qrXJmKw1OL1Y3MIrEiX6Ie4spkW8JNIlb8ZVfPN 2a7 Nam4zNu/L4SUkM5uThl0iR+JQYY4ndveIJ8YkXu2Le+ScAAHDFIjaGZpuRK3mKV7U2WcqMOMOke/pWu7VZrU2ct7VXTpST3e3jjeWYM/b +a9 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 6A6 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 3a7 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 0a+ QzH6nhj6MGT7ilVxZKJl8dxiu/+3O 8a3 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 ... src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDUAAAXeCAIAAADw5oNDAAAACXBIWXMAABYlAAAWJQFJUiTwAAAgAElEQVR42uzdu1Oc63 3A8 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 2a7 O4jP5gZaa900wp9fX1FV/S3mm+fjofgyEppcrd+7e+WU4p3f5xuzJ6L4+NdPaed/7vf2//uF0cLensPT/440Tn1V68pDJ6L27oEwAA+HoMz63FsvXYXCuWoQ9MLUQbxIL4V4/rpRUjsTXw/qORGDPJp6C0W5u9V6eklE52t/Pp8vHyo/WlwenFmFcWmXS0vhQr40sTumJUJzYprtYmu9ex6BMAALipSn/9R1oMzTartYmT3e1Yi3K0vtTZe368sZxjIJaORCoUz4yPJSsDUwsDUwt3Hr4sfVBunpRScSOvYmPEpsbxiZEx+f3zmvvixLPip+sTAAC42d5bMbK7HVO2qrXJmKw1OL1Y3MIrEiX6Ie4spkW8JNIlb8ZVfPN 2a7 Nam4zNu/L4SUkM5uThl0iR+JQYY4ndveIJ8YkXu2Le+ScAAHDFIjaGZpuRK3mKV7U2WcqMOMOke/pWu7VZrU2ct7VXTpST3e3jjeWYM/b +a9 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 6A6 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 3a7 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 0a+ QzH6nhj6MGT7ilVxZKJl8dxiu/+3O 8a3 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...

... District- and site-specific data on achievementrelated data are collected and disaggregated11.3 Standards and competency-related data arecollected and disaggregated11.4 Counselors are accountable ... discrepancies are aligned withthe ASCA National Standards 12.4 The identified needs become sources for thedetermination of closing-the-gap activitiesProgram Audit THE ASCA NATIONAL MODEL: A FRAMEWORK ... skills that are observable and can be transferred from a learning sit-uating to a real-life situation and that involve the production of a measurable outcome. Competencies areindicators that a student...

... process of lex-ical chains extraction can be done automatically.However the algorithms on lexical chains extrac-tion usually use WordNet to calculate the related-ness, but as it was already mentioned ... consideredonly figurative language detection issues: The al-gorithm marks an instance as a figurative usage iffthe instance is labeled as an outlier. Thus, we mea-sure the accuracy of the algorithm as follows:precision ... figurative language (metaphors,metonymies etc.). However, there are some worksaimed at extracting particular types of figura-tive language. For example, Nissim and Mark-ert (2003) proposed a machine...

... presentation at differ- ent levels: macro-planning, micro-planning , re- alization, and formatting. PRESENTOR is im- plemented and is portable cross-platform and cross-domain. It has been ... techniques facilitating a rapid development and customization of the presentations accord- ing to particular standards or preferences. Typ- ically, the overall task of generating a presen- tation ... conceptual representations can be unabashedly domain-specific, which is necessary in applica- tions, since a broad-coverage implementation of a domain-independent theory of conceptual rep- resentations...