Tootfinder

How the US democracy is designed to avoid representation
Right now in the US, a system which proclaims to give each citizen representation, my interests are not represented very well by most of my so-called representatives at any level of government. This is true for a majority of Americans across the political spectrum, and it happens by design. The "founding fathers" were explicit about wanting a system of government that would appear Democratic but which would keep power in the hands of rich white landowners, and they successfully designed exactly that. But how does disenfranchisement work in this system?
First, a two-party system locked in by first-post-the-post winner-takes-all elections immediately destroys representation for everyone who didn't vote for the winner, including those who didn't vote or weren't eligible to vote. Single-day non-holiday elections and prisoner disenfranchisement go a long way towards ensuring working-class people get no say, but much larger is the winner-takes all system. In fact, even people who vote for the winning candidate don't get effective representation if they're really just voting against the opponent as the greater of two evils. In a 51/49 election with 50% turnout, you've immediately ensured that ~75% of eligible voters don't get represented, and with lesser-of-two-evils voting, you create an even wider gap to wedge corporate interests into. Politicians need money to saturate their lesser-of-two-evils message far more than they need to convince any individual voter to support their policies. It's even okay if they get caught lying, cheating, or worse (cough Epstein cough) as long as the other side is also doing those things and you can freeze out new parties.
Second, by design the Senate ensures uneven representation, allowing control of the least-populous half of states to control or at least shut down the legislative process. A rough count suggests 284.6 million live in the 25 most-populous states, while only 54.8 million live in the rest. Currently, counting states with divided representation as two half-states with half as much population, 157.8 million people are represented by 53 Republican sensors, while 180.5 million people get only 45 seats of Democratic representation. This isn't an anti-Democrat bias, it's a bias towards less-populous states, whose residents get more than their share it political power.
I haven't even talked about gerrymandering yet, or family/faith-based "party loyalty," etc. Overall, the effect is that the number of people whose elected representatives meaningfully represent their interests on any given issue is vanishingly small (like, 10% of people tops), unless you happen to be rich enough to purchase lobbying power or direct access.
If we look at polls, we can see how lack of representation lets congress & the president enact many policies that go against what a majority of the population wants. Things like abortion restrictions, the current ICE raids, and Medicare cuts are deeply unpopular, but they benefit the political class and those who can buy access. These are possible because the system ensures at every step of the way that ordinary people do NOT get the one thing the system promises them: representation in the halls of power.
Okay, but is this a feature of all democracies, inherent in the nature of a majority-decides system? Not exactly...
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#uspol #democracy

A Systematic Mapping Study on Open Source Agriculture Technology Research
Kevin Lumbard, Vinod Kumar Ahuja, Matt Cantu Snell
https://arxiv.org/abs/2507.08103

A Systematic Mapping Study on Open Source Agriculture Technology Research
Agriculture contributes trillions of dollars to the US economy each year. Digital technologies are disruptive forces in agriculture. The open source movement is beginning to emerge in agriculture technology and has dramatic implications for the future of farming and agriculture digital technologies. The convergence of open source and agriculture digital technology is observable in scientific research, but the implications of open source ideals related to agriculture technology have yet to be ex…

Toward better understanding of energy in economics: Improvements to the Garrett thermodynamic economic model yield a robust system
Brian P. Hanley
https://arxiv.org/abs/2508.08723

Toward better understanding of energy in economics: Improvements to the Garrett thermodynamic economic model yield a robust system
In a series of papers, Garrett, et al, presents a thermodynamic economic model first laid out in "Are there basic physical constraints on future anthropogenic emissions of carbon dioxide?". This model contains a key conceptual issue that obscures a robust system. This system can link to the Energy Based Cobb-Douglas equation. The key conceptual problem is the belief that $λ$, the symbol for growth in Garrett 2011 would disprove the model if it was not constant. However, $λ$ cannot be a cons…

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

The challenge of HEPA filters in the classrooms.
h/t @…
source: https://xcancel.com/kadamssl/status/1938267359233429683#m

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Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes
Neelesh Rampal, Peter B. Gibson, Steven C. Sherwood, Laura E. Queen, Hamish Lewis, Gab Abramowitz
https://arxiv.org/abs/2507.06527

Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes
The computational cost of dynamical downscaling limits ensemble sizes in regional downscaling efforts. We present a newly developed generative-AI approach to greatly expand the scope of such downscaling, enabling fine-scale future changes to be characterised including rare extremes that cannot be addressed by traditional approaches. We test this approach for New Zealand, where strong regional effects are anticipated. At fine scales, the forced (predictable) component of precipitation and temper…

Today's Experiments Suffice to Verify the Quantum Essence of Gravity
Martin Pl\'avala
https://arxiv.org/abs/2508.03052 https://arxiv.org/pdf/2508.0…

Today's Experiments Suffice to Verify the Quantum Essence of Gravity
The gravity-mediated entanglement experiments employ concepts from quantum information to argue that if gravitational interaction creates entanglement between two systems, then gravity cannot be described by a classical system. However, the proposed experiments remain beyond out current technological capability, with optimistic projections placing the experiment outside of short-term future. Here we leverage quantum information techniques to argue that current matter-wave interferometers are su…

A portrait throughout perihelion of the NH$_2$-rich interstellar comet 2I/Borisov
Sophie E. Deam, Michele T. Bannister, Cyrielle Opitom, Matthew M. Knight, Ryan Ridden-Harper, Darryl Z. Seligman, Alan Fitzsimmons, Aur\'elie Guilbert-Lepoutre, Emmanuel Jehin, Laurent Jorda, Michael Marsset, Youssef Moulane, Philippe Rousselot, Pierre Vernazza, Bin Yang

A portrait throughout perihelion of the NH$_2$-rich interstellar comet 2I/Borisov
The interstellar comet 2I/Borisov is the first interstellar object where compositional characterisation was possible throughout its entire perihelion passage. We report all 16 epochs of a comprehensive optical observation campaign with ESO VLT's integral field spectrograph MUSE, spanning 126 days from 2019 November 14 to 2020 March 19. The spatial dust emission of 2I/Borisov was predominantly smooth, with no seasonal effect. A jet-like feature was consistently visible. The gas production morpho…

SAFERad: A Framework to Enable Radar Data for Safety-Relevant Perception Tasks
Tim Br\"uhl, Jenny Gl\"onkler, Robin Schwager, Tin Stribor Sohn, Tim Dieter Eberhardt, S\"oren Hohmann
https://arxiv.org/abs/2507.03959

SAFERad: A Framework to Enable Radar Data for Safety-Relevant Perception Tasks
Radar sensors play a crucial role for perception systems in automated driving but suffer from a high level of noise. In the past, this could be solved by strict filters, which remove most false positives at the expense of undetected objects. Future highly automated functions are much more demanding with respect to error rate. Hence, if the radar sensor serves as a component of perception systems for such functions, a simple filter strategy cannot be applied. In this paper, we present a modified…