Just #SurveillanceCapitalism things. Absolute creep behaviour. And this is why I try hard not to actually use the Crimson card and just transfer money to my debit card ASAP instead. Because knowing #DoorDash, they'll track when you top off your tank and skew you towards shitty offers, knowing you might be more apt to take them at the top of the tank than the bottom. #gigwork #GigWorker #surveillance #capitalism
![Android notification from DoorDash.
Upcoming bill [redacted]
Looks like your [redacted] bill is around the corner. Dash today and stay ahead with confidence and one less thing on your mind.](https://cdn.tenforward.social/media_attachments/files/114/915/380/890/672/089/original/dcacaaa791b46f53.jpg "Android notification from DoorDash.
Upcoming bill [redacted]
Looks like your [redacted] bill is around the corner. Dash today and stay ahead with confidence and one less thing on your mind.")
@foreverandaday That's exactly what it is. You have to do 100 deliveries within the past 30 days, *and* accept 70% of the offers they give to you, in order to keep platinum status. In theory, platinum gives you "first dibs" on good offers. In practice, no the fuck it doesn't. #DoorDash #GigWork
"Scale AI is basically a data annotation hub that does essential grunt work for the AI industry. To train an AI model, you need quality data. And for that data to mean anything, an AI model needs to know what it's looking at. Annotators manually go in and add that context.
As is the means du jour in corporate America, Scale AI built its business model on an army of egregiously underpaid gig workers, many of them overseas. The conditions have been described as "digital sweatshops," and many workers have accused Scale AI of wage theft.
It turns out this was not an environment for fostering high-quality work.
According to internal documents obtained by Inc, Scale AI's "Bulba Experts" program to train Google's AI systems was supposed to be staffed with authorities across relevant fields. But instead, during a chaotic 11 months between March 2023 and April 2024, its dubious "contributors" inundated the program with "spam," which was described as "writing gibberish, writing incorrect information, GPT-generated thought processes."
In many cases, the spammers, who were independent contractors who worked through Scale AI-owned platforms like Remotasks and Outlier, still got paid for submitting complete nonsense, according to former Scale contractors, since it became almost impossible to catch them all. And even if they did get caught, some would come back by simply using a VPN.
"People made so much money," a former contributor told Inc. "They just hired everybody who could breathe.""
Immer mehr Menschen bieten ihre Arbeit über digitale Plattformen an. Das bringt Freiheiten, aber auch Einkommensunsicherheit mit sich. Der Berner Arbeitspsychologe Daniel Spurk hat genauer hingeschaut: https://www.uniaktuell.unibe.ch/2025/gig_worker_so_leben_und_arbeiten_sie_wirklich/index_ger.html.
#GigWork #Arbeit
@barefootstache @philip For mapping out the surveillance items specifically, I've been working with DeFlock, which unfortunately only does its work inside of Discord :( The tagging schema is evolving, but has largely coalesced around something like this for Flock devices:
```
camera:mount=pole
camera:type=fixed
direction=150
electricity=solar
man_made=surveillance
manufacturer=Flock Safety
manufacturer:wikidata=Q108485435
surveillance=outdoor
surveillance:type=ALPR
surveillance:zone=traffic
```
The `electricity` key can take either `solar` or `grid` as values. `surveillance:type` key's value is either `ALPR` or `gunshot_detector`, depending on the device. `surveillance:zone` key's value is typically either `traffic` or `parking`, though `entrance` can sometimes be relevant (e.g., ALPRs pointed at the gated entrances to apartment complexes).
Additional tags that are sometimes included are the `operator` and `operator:wikidata` tags. Typically, if a public info request is successfully made, it'll say who leases the cameras and other devices from Flock, and that becomes the `operator` (typically a police department). Or, if a particular entity is known to use Flock devices, such as Home Depot --- they use them across at least some of their stores' parking lots --- then that would become the operator. Ultimately though, most operators end up sharing their data with others, so who the specific operator is isn't that pertinent IMHO.
I don't think anything has been added to the OSM wiki though. Maybe I should get this info on there, since there, it will outlast any ephemeral chatter in a Discord server.
Actually, there is some guidance for Flock in Richmond, Virginia:
https://wiki.openstreetmap.org/wiki/Richmond,_Virginia/Tags#Flock_Safety
Might introduce a dedicated page to the wiki though, to get/keep everyone on the same page.
As for workflow:
1. Pick an area to survey. I do #GigWork #DoorDash, so I drive all over the place regularly. When I see a Flock device, I note the area down for follow-up survey.
2. Drive around the area, recording path with #OsmAnd (for GPX files).
3. Upon seeing a device, find a safe place to pull over.
4. Use #StreetComplete to add an initial `surveillance` (shot spotters) or `surveillance camera` (ALPRs) thing.
5. Add a note via StreetComplete with more info on the device, including orientation, and a picture if possible.
6. Go home.
7. Pull up iD, and go through my notes, fleshing out the tags on all the surveillance nodes I added early via StreetComplete.
About 11,900,000 people is 3.5% of the United States population, which I understand is a critical tipping point for protest to make a difference.
Average that as 6000 people at 2000+ sites -- who thinks we're getting close today?
Big cities have more folks, smaller cities have fewer…
They moved the protest in our town to a park that has an edge along a very busy street, so I got to wave and honk on my way from one job to another. 
I am slowly beginning to understand the frustration that leads people to crime, it seems. Even the #GigWork platforms like #DoorDash are starting to dry up on me. Thankfully, I have a built-in safety net in the form of loving family.
And if I didn't? Well, I might not be gay, but I'd probably be doing crime at this point.
Gig companies "leave workers at the mercy of unstable and subminimum wages, little social protection, and in constant fear of termination without recourse" despite meeting criteria for employee status
https://www.hrw.org/news/2025/05/12/us-major-companies-violate-gig-workers-rights
It's 2025 and Numbers.app (Apple's Excel-type app) can't do a two-axis graph with a stacked column? Really?
Boo...
Well anyway, I guess if I want to see my weekly #DasherLife earnings vs. time this is the best I can do! I refuse to submit to Excel :D
I'll clean it up later… nothing really surprising here. Everything is grouped by week with the blue bar being the total earned for that week and the components of that total beside.
#GigWork #DoorDash #UberEats
I've been trying out doing double-duty delivery, Door Dash and Uber Eats, at the same time.
There are a number of problems.
1) In my small town, DD still rules, there just aren't the same number of UE. Long stretches of the night will go without a single UE order.
2) Juggling the two apps is possible, but you're left hoping that an order from DD is in the same direction as one from UE. They usually aren't.
3) Because of 2), you have to choose whether to pause or go offline, which (I think) impacts the algorithm and leads to fewer orders, or less valuable ones.
4) Customers really don't like (understandably) if they see you veering off route to another store (even when its an order on the same app!) so ratings are at even bigger risk if you don't make the timings.
5) UE seems to have far fewer tips, so overall, if it werent for the $15 "quests" of extra cash to complete an order the pay is not as good.
6) This is all more stressful.
Conclusion:
I think I'm just gonna stick to DD exclusively for now unless daily totals start going down or its a super slow night.
#App-basierte #Lieferdienste:
#Wegwerfjobs für #marginalisierte #Menschen
Sogenannte #GigWork hat zu #Recht einen schlechten Ruf. Dennoch ist der #Arbeitssektor in den vergangenen Jahren stark gewachsen. Eine #repräsentative #Studie hat nun herausgefunden, warum Menschen solche Jobs überhaupt annehmen – und warum sie so oft schnell wieder kündigen.
"#Uberfication" encroaching on #teaching profession, including at #Oxford, leaves teachers scrambling for precarious #work
Nach dem Vorbild von #Uber oder #Glovo melden sich Pflege- und Gesundheitsfachkräfte bei KI-gesteuerten Apps an, um temporäre Arbeit in Krankenhäusern, medizinischen Zentren und Haushalten zu finden.
In einer von #GigWork geprägten Wirtschaft reproduzieren sie viele der strukturellen Probleme, die auch andere KI-Branchen betreffen.
Diese Woche in #AutomatedSociety untersuchen wir, wie diese Plattformen in Europa an Bedeutung gewinnen. Abonniere jetzt den Newsletter: https://automatedsociety.algorithmwatch.org/#/de
Mirroring the dynamics of platforms like #Uber or #Glovo, nurses, healthcare professionals and daycare workers sign up to algorithm-managed apps that connect them to hospitals, medical centres and even households in need of temporary staff. Born into an economy shaped by #GigWork, these apps reproduce many of the same structural problems.
This week in #AutomatedSociety, we examine how these platforms are gaining traction in Europe. Subscribe to our newsletter now: https://automatedsociety.algorithmwatch.org/#/en/
Vile Startup Charges Gig Workers to Get Paid on Time
https://futurism.com/the-byte/startup-charges-gig-workers-for-wages
