BioJam Camp: toward justice through bioengineering and biodesign
co-learning with youth
- URL: http://arxiv.org/abs/2211.06313v1
- Date: Tue, 1 Nov 2022 21:10:56 GMT
- Title: BioJam Camp: toward justice through bioengineering and biodesign
co-learning with youth
- Authors: Callie Chappell, Henry A.-A., Elvia B. O., Emily B., Bailey B.,
Jacqueline C.-M., Caroline Daws, Cristian F., Emiliano G., Page Goddard,
Xavier G., Anne Hu, Gabriela J., Kelley Langhans, Briana Martin-Villa, Penny
M.-S., Jennifer M., Soyang N., Melissa Ortiz, Aryana P., Trisha S, Corinne
Takara, Emily T., Paloma Vazquez, Rolando Perez, Jen Marrero Hope
- Abstract summary: BioJam is a political, artistic, and educational project in which Bay Area artists, scientists, and educators collaborate with youth and communities of color to address historical exclusion of their communities in STEM fields and reframe what science can be.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: BioJam is a political, artistic, and educational project in which Bay Area
artists, scientists, and educators collaborate with youth and communities of
color to address historical exclusion of their communities in STEM fields and
reframe what science can be. As an intergenerational collective, we co-learn on
topics of culture (social and biological), community (cultural and ecological),
and creativity. We reject the notion that increasing the number of scientists
of color requires inculcation in the ways of the dominant culture. Instead, we
center cultural practices, traditional ways of knowing, storytelling, art,
experiential learning, and community engagement to break down the framing that
positions these practices as distinct from science. The goal of this work is to
realize a future in which the practice of science is relatable, accessible, and
liberatory.
Related papers
- Two Heads Are Better Than One: A Multi-Agent System Has the Potential to Improve Scientific Idea Generation [48.29699224989952]
VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas.
We show that this multi-agent approach outperforms the state-of-the-art method in producing novel and impactful scientific ideas.
arXiv Detail & Related papers (2024-10-12T07:16:22Z) - A Vision on Open Science for the Evolution of Software Engineering Research and Practice [40.07325268305058]
Open Science aims to foster openness and collaboration in research, leading to more significant scientific and social impact.
practicing Open Science comes with several challenges and is currently not properly rewarded.
arXiv Detail & Related papers (2024-05-20T15:51:23Z) - CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting [73.94059188347582]
We uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations.
We discover that culture-conditioned generation consist of linguistic "markers" that distinguish marginalized cultures apart from default cultures.
arXiv Detail & Related papers (2024-04-16T00:50:43Z) - How should the advent of large language models affect the practice of
science? [51.62881233954798]
How should the advent of large language models affect the practice of science?
We have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate.
arXiv Detail & Related papers (2023-12-05T10:45:12Z) - Getting aligned on representational alignment [93.08284685325674]
We study the study of representational alignment in cognitive science, neuroscience, and machine learning.
Despite their overlapping interests, there is limited knowledge transfer between these fields.
We propose a unifying framework that can serve as a common language for research on representational alignment.
arXiv Detail & Related papers (2023-10-18T17:47:58Z) - Computer Science [0.0]
Science and technology are viewpoints diverse by either individual, community, or social.
Issues arise in either its theory or implementation, adapting different communities, or designing curriculum holds in the education system.
arXiv Detail & Related papers (2022-07-16T10:54:57Z) - Co-constructing Shared Values and Ethical Practice for the Next
Generation: Lessons Learned from a Curriculum on Information Ethics [0.0]
We present the motivation, design, outline, and lessons learned from an online course in scientific integrity, research ethics, and information ethics.
The goal of such a training is not so much to equip students, but to make them aware of the impact of their work on society.
While we provide conceptual tools, this is more to sustain interest and engage students.
arXiv Detail & Related papers (2022-04-06T11:09:23Z) - Seeing biodiversity: perspectives in machine learning for wildlife
conservation [49.15793025634011]
We argue that machine learning can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species.
In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies.
arXiv Detail & Related papers (2021-10-25T13:40:36Z) - Towards decolonising computational sciences [0.0]
We see this struggle as requiring two basic steps.
grappling with our fields' histories and heritage holds the key to avoiding mistakes of the past.
We aspire for these fields to progress away from their stagnant, sexist, and racist shared past.
arXiv Detail & Related papers (2020-09-29T18:48:28Z) - Building a Framework for Indigenous Astronomy Collaboration: Native
Skywatchers, Indigenous Scientific Knowledge Systems, and The Bell Museum [0.0]
This document is the process of building a framework for developing Indigenous astronomy programming.
It can be a model for other institutions that may be interested in collaborating with Indigenous communities.
arXiv Detail & Related papers (2020-08-12T12:32:32Z) - Leveraging traditional ecological knowledge in ecosystem restoration
projects utilizing machine learning [77.34726150561087]
Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being.
We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
arXiv Detail & Related papers (2020-06-22T16:17:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.