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Indigenous Epistemologies & Open Science: Learning from the Land

1h 7m · Unsettling Knowledge Inequities · 26 Oct 04:00

In November 2020, the world’s first Virtual Indigenous Circle on Open Science and the Decolonization of Knowledge took place.  The Circle format was designed by Dr Lorna Wanósts’a7 Williams and featured nearly 20 Indigenous speakers from around the world. 
 
They came together to inform UNESCO’s recommendation on Open Science and ensure that Indigenous knowledge and perspectives would be incorporated respectfully and with integrity into the recommendation.

In this episode, four of those participants (Lorna Wanósts’a7 Williams, Greg Cajete, Manulani Aluli Meyer, and Sonajharia Minz) have gathered again to extend that conversation and further speak to Indigenous epistemologies, their personal journeys in science and academia, and many vital reflections on being attuned to the quality of our relationships, changing our consciousness, cultivating a sense of reverence, and much much more. 

 

The episode Indigenous Epistemologies & Open Science: Learning from the Land from the podcast Unsettling Knowledge Inequities has a duration of 1:07:26. It was first published 26 Oct 04:00. The cover art and the content belong to their respective owners.

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Resources mentioned in episode:

  1. https://www.gapminder.org/dollar-street 
  2. https://people.clarkson.edu/~jmatthew/publications/Wali_ParticipatoryML_ICML2020.pdf
  3. https://logicmag.io/beacons/the-oversight-bloc/


Other relevant resources: 

  1. Datasets have Worldviews https://pair.withgoogle.com/explorables/dataset-worldviews/
  2. Measuring Diversity https://pair.withgoogle.com/explorables/measuring-diversity/
  3. Milagros Miceli, Tianling Yang, Adriana Alvarado Garcia, Julian Posada, Sonja MeiWang, Marc Pohl, and Alex Hanna. 2022. Documenting Data Production Processes: A Participatory Approach for Data Work. Proc. ACM Hum.-Comput. Interact. 6, CSCW2 (August 2022), 34 pages.https://arxiv.org/abs/2207.04958
  4. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
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