Indigenous frameworks for data-intensive humanities: recalibrating the past through knowledge engineering and generative modelling. - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Journal of Data Mining and Digital Humanities Year : 2021

Indigenous frameworks for data-intensive humanities: recalibrating the past through knowledge engineering and generative modelling.

Abstract

Identifying, contacting and engaging missing shareholders constitutes an enormous challenge for Māori incorporations, iwi and hapū across Aotearoa New Zealand. Without accurate data or tools to har-monise existing fragmented or conflicting data sources, issues around land succession, opportunities for economic development, and maintenance of whānau relationships are all negatively impacted. This unique three-way research collaboration between Victoria University of Wellington (VUW), Parininihi ki Waitotara Incorporation (PKW), and University of Auckland funded by the National Science Challenge | Science for Technological Innovation catalyses innovation through new digital humanities-inflected data science modelling and analytics with the kaupapa of reconnecting missing Māori shareholders for a prosperous economic, cultural, and socially revitalised future. This paper provides an overview of VUW's culturally-embedded social network approach to the project, discusses the challenges of working within an indigenous worldview, and emphasises the importance of decolonising digital humanities.
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Dates and versions

hal-02461884 , version 1 (31-01-2020)
hal-02461884 , version 2 (11-02-2020)
hal-02461884 , version 3 (29-05-2020)
hal-02461884 , version 4 (14-12-2020)

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Sydney Shep, Marcus Frean, Rhys Owen, Rere-No-A-Rangi Pope, Pikihuia Reihana, et al.. Indigenous frameworks for data-intensive humanities: recalibrating the past through knowledge engineering and generative modelling.. Journal of Data Mining and Digital Humanities, 2021, HistoInformatics, ⟨10.46298/jdmdh.6095⟩. ⟨hal-02461884v4⟩
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