IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v175y2024i3d10.1007_s11205-023-03254-y.html
   My bibliography  Save this article

Data Quality and Violence Against Women: The Causes and Actors of Femicide

Author

Listed:
  • Alessia Forciniti

    (Iulm University)

  • Emma Zavarrone

    (Iulm University)

Abstract

The paper examines domestic ’femicide’ in Italy. Under an exploratory statistical approach, we investigated: (1) difficulties and strategies for reconstructing a historical dataset on family crimes for studies over time; (2) the main causes of family femicides; and (3) groups of actors driven by the same motivations interpreted as patterns of criminal behavior. First, we integrated and systematised data from official sources to guarantee comparison over time; second, we used Social Network Analysis properties to study the relationships between ’motivations’ and ’victim-perpetrator’; and third, we applied and compared community detection algorithms to the linkages between ’actors’ and ’motivations’ to detect groups of criminal behavior. From 2015 to 2020 in Italy, the cohabitant was the major family murderer, but in 2020, passion motivation also surfaced. Mental problems connected to parents-children and cohabitants, jealousy of ex-partners or rivals, and economic issues for blood relations were observed in 2015. Psychopathologies and money characterised parents-children in 2020, while passion and disagreements caused cohabitants or ex-partners.

Suggested Citation

  • Alessia Forciniti & Emma Zavarrone, 2024. "Data Quality and Violence Against Women: The Causes and Actors of Femicide," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(3), pages 1073-1097, December.
    • Handle: RePEc:spr:soinre:v:175:y:2024:i:3:d:10.1007_s11205-023-03254-y
    DOI: 10.1007/s11205-023-03254-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-023-03254-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11205-023-03254-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Serguei Saavedra & Daniel B. Stouffer & Brian Uzzi & Jordi Bascompte, 2011. "Strong contributors to network persistence are the most vulnerable to extinction," Nature, Nature, vol. 478(7368), pages 233-235, October.
    2. Michael T. SCHAUB & Jean-Charles DELVENNE, 2017. "The many facets of community detection in complex networks," LIDAM Reprints CORE 2890, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Yao Hongxing & Lu Yunxia, 2017. "Analyzing the Potential Influence of Shanghai Stock Market Based on Link Prediction Method," Journal of Systems Science and Information, De Gruyter, vol. 5(5), pages 446-461, October.
    3. Chang, Y.F. & Han, S.K. & Wang, X.D., 2018. "The way to uncover community structure with core and diversity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 111-119.
    4. Antonios Garas & Celine Rozenblat & Frank Schweitzer, 2015. "The network structure of city-firm relations," Papers 1512.02859, arXiv.org.
    5. Gergely Tibély & David Sousa-Rodrigues & Péter Pollner & Gergely Palla, 2016. "Comparing the Hierarchy of Keywords in On-Line News Portals," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-15, November.
    6. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    7. Michel Alexandre & Felipe Jordão Xavier & Thiago Christiano Silva & Francisco A. Rodrigues, 2022. "Nestedness in the Brazilian Financial System," Working Papers Series 566, Central Bank of Brazil, Research Department.
    8. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    9. Liu, Chuang & Zhou, Wei-Xing, 2012. "Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5704-5711.
    10. Tamás Nepusz & Tamás Vicsek, 2013. "Hierarchical Self-Organization of Non-Cooperating Individuals," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    11. Xia, Yongxiang & Pang, Wenbo & Zhang, Xuejun, 2021. "Mining relationships between performance of link prediction algorithms and network structure," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    12. Sebastián Bustos & Charles Gomez & Ricardo Hausmann & César A Hidalgo, 2012. "The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
    13. Nora Connor & Albert Barberán & Aaron Clauset, 2017. "Using null models to infer microbial co-occurrence networks," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-23, May.
    14. Aslan, Serpil & Kaya, Buket & Kaya, Mehmet, 2019. "Predicting potential links by using strengthened projections in evolving bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 998-1011.
    15. Xinyi Liu & Bin Liu & Zhimin Huang & Ting Shi & Yingyi Chen & Jian Zhang, 2012. "SPPS: A Sequence-Based Method for Predicting Probability of Protein-Protein Interaction Partners," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-6, January.
    16. Rosanna Grassi & Paolo Bartesaghi & Stefano Benati & Gian Paolo Clemente, 2021. "Multi-Attribute Community Detection in International Trade Network," Networks and Spatial Economics, Springer, vol. 21(3), pages 707-733, September.
    17. Vanessa Ioannoni & Tommaso Vitale & Corrado Costa & Iris Elliott, 2020. "Depicting communities of Romani studies: on the who, when and where of Roma related scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1473-1490, March.
    18. Amulyashree Sridhar & Sharvani GS & AH Manjunatha Reddy & Biplab Bhattacharjee & Kalyan Nagaraj, 2019. "The Eminence of Co-Expressed Ties in Schizophrenia Network Communities," Data, MDPI, vol. 4(4), pages 1-23, November.
    19. Li, Qing & Zhang, Huaige & Hong, Xianpei, 2020. "Knowledge structure of technology licensing based on co-keywords network: A review and future directions," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 154-165.
    20. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:soinre:v:175:y:2024:i:3:d:10.1007_s11205-023-03254-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.