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12 Nov, 5 tweets, 5 min read
At the @QUTDataScience Data Science for Social Good showcase, @oforbes22 sharing about ways to visualize spatial uncertainty for the Cancer Atlas map, using glyphs or hues & whiteness in a project with @CCQld Image
This has been the inaugural year for @QUTDataScience Data Science for Social Good, with grad students & recent grads partnering with 2 non-profits: @CCQld Cancer Atlas & @fareshare_aus Qld food charity

@KerrieMengersen & @Farzana18_jahan kicking off our showcase ImageImage
Mapping food insecurity in Queensland with an interactive map @fareshare_aus (largest meal charity in 🇦🇺) & @QUTDataScience Image
I enjoyed this! Thank you to @charleemoyle & @timothyjgraham for such thoughtful & nuanced questions, including tackling some of the ethical risks & pitfalls to be aware of with data for social good. And great discussion from the other panelists!

@QUTDataScience is inviting more projects from non-profits & social enterprises for the next round!

For more details & to contact us, please visit: research.qut.edu.au/qutcds/dssg/ Image

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More from @math_rachel

Rachel Thomas Profile picture
13 Nov
"Who benefits from data sharing in Africa? What barriers exist in the data sharing ecosystem, and for whom? If much of the data sharing practice is shaped by the Global North, how can we ensure that the narrative for Africa is controlled by Africans?" 1/

arxiv.org/abs/2103.01168 Narratives and Counternarratives on Data Sharing in Africa R
Stakeholders in the African data sharing ecosystem. Those at the top of the iceberg hold significant power & leverage in guiding data sharing practices & policy compared to those in the hidden part of the iceberg. More powerful stakeholders wield disproportionate power. 2/ picture of iceberg. From top: (above water) government bodie
Dominant narratives around data sharing in Africa often focus on lack, insufficiency, deficit.

This framing minimizes the strength, agency, and scientific & cultural contributions of communities within the continent, and overlooks community norms, values, & traditions. 3/ a lack of knowledge about the value of data and training, as
Read 7 tweets
Rachel Thomas Profile picture
13 Nov
🧵automation of gov social services (eg food benefits, disability services, unemployment, etc) can be:
- implemented with no way to correct errors (software treated as error-free)
- smokescreen for policy changes
- justify austerity under guise of efficiency
- operate at scale 1/
In France, updates to an automated system for benefit payments caused errors, delays, & incorrect debts for at least 60,000 people

Case workers are unable to correct errors in the system. Some victims coped by *cutting back on food* 2/

hrw.org/news/2021/11/1… @hrw In France, the Caisse des Allocations Familiale (CAF), the g
Flawed algorithm in UK ignores how often ppl get paid and has led to people going hungry & falling into debt

This is not just a technical error; the government deliberately chose this method of calculation because it was easier to automate, increased efficiency, & reduced costs In the United Kingdom, the government is using a flawed algo
Read 12 tweets
Rachel Thomas Profile picture
12 Nov
Surveillance is best understood not simply as watching & monitoring, but as a calculated practice for managing & manipulating human behaviour.

Surveillance as governance: to what ends is surveillance undertaken, what forms of power operate. 1/

journals.sagepub.com/doi/10.1177/02… @pwh67 As Foucault’s examination of discipline suggests, surveillTo focus exclusively on surveillance is therefore too narrow
Classifications are not neutral. The way in which categories are defined and who defines them tell a story of power. 2/ attention to the ways they segment and classify populations.
Increasingly, populations are segmented & differentially treated. Surveillance sorts people into categories, assigning worth or risk, in ways that have real effects on their life chances. 3/ However, the wide-scale nature of much surveillance practice
Read 5 tweets
Rachel Thomas Profile picture
10 Nov
"Data do not speak for themselves. Data must be narrated—put to work in particular contexts, sunk into narratives that give them shape and meaning, and mobilized as part of broader processes of interpretation and meaning-making." @dourish @Imagenaciones

journals.sagepub.com/doi/10.1177/20… Datafication and data fiction: Narrating data and narrating
Two scalar moves in data science:
1. move datum➡️ data set, the claim that these data are sufficiently "alike" as to be able to be combined, compared, added, & divided
2. move large ➡️ small implicit in the drawing of conclusions or categories from data analysis 2/ We think of the relationship between data and narrative in t
The granularity of the data, both spatially and temporally, radically reconfigured the work that they had to do. The very fact of a digital trace produced the necessity of an account, leaving them with less time for their previous responsibilities to parolees and to the public 3/ their work. What is more, the granularity of the data, both
Read 5 tweets
Rachel Thomas Profile picture
30 Oct
As a mother, I can't wait until my 6 year old can be vaccinated against covid. My #1 reason is that I hope to reduce her chances of getting long covid or suffering any long-term effects. I want to share some of my personal thoughts here. 1/
First, in the USA the FDA has approved the vaccine for 5-11 year olds. This vaccine has been incredibly well studied and is safe. 2/
theguardian.com/world/2021/oct…
It will take years before we fully understand the long-term consequences of covid, but the studies so far (primarily in adults) of damage to a variety of organs, including the brain, vascular system, and immune system are deeply disturbing. 3/
Read 16 tweets
Rachel Thomas Profile picture
26 Oct
- Millions of people are disabled with long covid (a horrific illness)
- This is going to be VERY expensive
- There are big financial incentives to downplay the severity & prevalence
- Concerted efforts similar to the climate denial playbook

fast.ai/2021/10/12/med… 1/
It is critical for physicians, scientists, & public health officials to realize they are not dealing with an orthodox scientific debate, but a well-funded sophisticated denialist campaign based on ideological & corporate interests -- @GYamey @gorskon

blogs.bmj.com/bmj/2021/09/13… 2/
Many of the lessons from climate denialism debates apply to covid as well 3/

Read 12 tweets

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