02391oam 2200505 450 991082634300332120190911103511.01-4522-6968-81-4522-6921-11-4833-8742-9(OCoLC)843115224(MiFhGG)GVRL8TPR(EXLCZ)99371000000033364020110922h20122012 uy 0engurun|---uuuuatxtccrSafe and peaceful schools addressing conflict and eliminating violence /John Winslade, Michael WilliamsThousand Oaks, Calif. Corwinc2012Thousand Oaks, California :Corwin,[2012]�20121 online resource (xii, 194 pages)Gale eBooksDescription based upon print version of record.1-4129-8675-3 Includes bibliographical references and index.Cover; Contents; Preface; Chapter 1 - Understanding Conflict in Schools; Chapter 2 - A Narrative Perspective; Chapter 3 - Counseling; Chapter 4 - Mediation; Chapter 5 - Peer Mediation; Chapter 6 - Restorative Conferencing; Chapter 7 - Restorative Practices; Chapter 8 - Circle Conversations; Chapter 9 - Undercover Anti-Bullying Teams; Chapter 10 - Guidance Lessons; Chapter 11 - ""Facing Up to Violence"" Groups; Chapter 12 - Putting It All Together; References; IndexThis text provides practical strategies for teaching conflict resolution skills that help prevent bullying and violence for a safe and peaceful school environment. Modes of practice include peer mediation, narrative counseling, circle conversations, undercover anti-bullying team, 'facing up to violence' groups, and restorative conferences.School violenceUnited StatesPreventionSchoolsUnited StatesSafety measuresConflict managementStudy and teachingUnited StatesSchool violencePrevention.SchoolsSafety measures.Conflict managementStudy and teaching371.7820973Winslade John1604967Williams MichaelMiFhGGMiFhGGBOOK9910826343003321Safe and peaceful schools3929969UNINA04443nam 22004455 450 991033825190332120251113183123.03-030-11566-610.1007/978-3-030-11566-1(CKB)4100000007817055(MiAaPQ)EBC5741616(DE-He213)978-3-030-11566-1(PPN)242824684(EXLCZ)99410000000781705520191027d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierResearch in Data Science /edited by Ellen Gasparovic, Carlotta Domeniconi1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (302 pages)Association for Women in Mathematics Series,2364-5741 ;173-030-11565-8 Preface -- N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin: Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors -- P. Mani, M. Vazquez, J. R. Metcalf-Burton, C. Domeniconi, H. Fairbanks, G. Bal, E. Beer, and S. Tari: The Hubness Phenomenon in High Dimensional Spaces -- F. P. Medina, L. Ness, M. Weber, and K. Y. Djima: Heuristic Framework for Multiscale Testing of the Multi-Manifold Hypothesis -- K. Leonard, Y. Zhou, X. Wang, and G. Heo: High-dimensional Multiple Scaled Data Analysis of Obstructive Sleep Apnea Study with Interdisciplinary Endeavor -- E. Munch and A. Stefanou: The L(infinity)-Cophenetic Metric for Phylogenetic Trees as an Interleaving Distance -- L. Ness: Inference of a Dyadic Measure and its Simplicia Geometry from Binary Feature Data and Application to Data Quality -- A. Genctav, M. Genctav, and S. Tari: A Non-local Measure for Mesh Saliency via Feature Space Reduction -- F. Seeger, A. Little, Y. Chen, T. Woolf, H. Cheng, and J. C. Mitchell: Feature Design for Protein Interface Hotspots using KFC2 and Rosetta -- R. Aroutiounian, K. Leonard, R. Moreno, R. Teufel: Geometry-Based Classification for Automated Schizophrenia Diagnosis -- N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin: Compressed Anomaly Detection with Multiple Mixed Observations -- A. Grim, B. Iskra, N. Ju, A. Kryshchenko, F. P. Medina, L. Ness, M. Ngamini, M. Owen, R. Paffenroth, and S. Tang: Analysis of Simulated Crowd Flow Exit Data: Visualization, Panic Detection, and Exit Time Convergence, Attribution and Estimation -- V. Adanova and S. Tari: A Data Driven Modeling of Ornaments. .This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas. Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data. Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community. The volume is suitable for researchers in data science in industry and academia. .Association for Women in Mathematics Series,2364-5741 ;17Computer scienceMathematicsMathematical Applications in Computer ScienceComputer scienceMathematics.Mathematical Applications in Computer Science.502.85Gasparovic Ellenedthttp://id.loc.gov/vocabulary/relators/edtDomeniconi Carlottaedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910338251903321Research in Data Science1733520UNINA