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

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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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