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22 Nov, 16 tweets, 6 min read
Many people have a false dichotomy that you are either FOR or AGAINST covid restrictions, with no nuance about the TYPE of restrictions or level of effectiveness, much less that eschewing all restrictions → hospitals collapse & lockdown more likely. 1/
There has been a lot of terrible public health messaging & contradictory government policies in the West, from the start of the pandemic, continuing now, and these erode public trust, create false expectations, & contribute to “pandemic fatigue” 2/
The “only elderly & chronically ill are at risk” was both false AND ineffective messaging. This has been clear from the VERY START of the pandemic. (I RTed @jenbrea at the time) 3/
“Why should vaxxed still care about covid??”
- Immunity wanes
- Breakthroughs are NOT rare
- Long Covid from breakthroughs is NOT rare
- Long Covid can entail multi-system organ damage & permanent disability 4/
Great article from @trishgreenhalgh highlighted that we didn’t just get bad advice in the UK (& USA), but that it was given with high certainty, undermining public trust when leaders had to backtrack later 5/

A lot of focus has been placed on “personal responsibility”, not so much on what we should expect from our employers, our governments, and our kids’ schools. Droplet transmission was false, but aligned well with neoliberal & libertarian ideology. 6/

authorea.com/users/316109/a…
Spending time in a building with great VENTILATION and AIR FILTERS will not be experienced as “restrictive” by most people. Governments will save money in the long-run by funding these upgrades. 7/

8 of 9 states in Austria got rid of mask mandates in July → covid is out of control now → they have to lock down

Masks are LESS RESTRICTIVE than lockdowns. Mask mandates help prevent lockdowns. 8/

Many people are still wearing low-quality, poor fitting masks (if wearing masks at all). Homemade cloth masks were supposed to be a stop-gap measure while countries ramped up mask production (article from Jan 2021) 9/

theatlantic.com/health/archive…
Governments set people up for misunderstanding & anger when they make promises that they can’t keep (eg promising that if you get vaxxed you can return to “normal” & never need to wear a mask again, ignoring breakthroughs, reinfections, & longcovid) 10/

theguardian.com/world/2021/oct…
Ineffective restrictions, like “deep cleans”, plexiglass barriers, hand sanitizer, and closing parks & beaches confuse people about how covid spreads (it’s AIRBORNE) and take away energy from more effective measures 11/

We already knew the emptiness of Hygiene Theater by July 2020 12/

theatlantic.com/ideas/archive/…
Zeynep did a great job covering “beach shaming" Dozens of articles focused on beach goers (outside in the sun is a relatively safe place to be) & ran deceptive photos taken with wide angle lenses to make people seem closer together than they were 13/
USA laws that let you meet with extended family in a restaurant but not your in own home were clearly contradictory & did not build public trust (from Nov 2020) 14/

theatlantic.com/health/archive…
Nobody wants restrictions just for the sake of restrictions. We should focus on highest-impact, most effective ones. This is anything that improves the quality of air people breathe, recognizing that vaccines alone aren't enough and that #COVIDisAirborne 15/
"Interventions by authorities can backfire if they fuel mistrust or treat the public as an adversary rather than people who will step up if treated with respect."

Re-reading why lying to public that masks don't work was a bad strategy, @zeynep March 2020
nytimes.com/2020/03/17/opi…

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More from @math_rachel

Rachel Thomas Profile picture
16 Nov
The false hope of current approaches to explainable AI in health care: current explainability approaches can produce broad descriptions of how an AI system works in general, but for individual decisions, the explanations are unreliable or superficial 1/
thelancet.com/journals/landi… The false hope of current approaches to explainable artifici
Explainability methods of complex AI systems can provide some insight into the decision making process on a global level. However, on an individual level, the explanations we can produce are often confusing or even misleading. @MarzyehGhassemi @DrLaurenOR @AndrewLBeam 2/ What are explanations for? These limitations do not render e
Increased transparency can hamper users’ ability to detect sizable model errors and correct for them, "seemingly due to information overload." 3/ The intuitive simplicity of inherently explainable models is
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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
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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
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
Read 5 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
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