civic science.

The height of modernity poured massive resources into science superprojects — but also seeded a countertrend of “citizen” or “civic” science (also “folk,” “cottage,” “exoteric”). Having devolved from antique universality (“anyone inclined to think about nature was once qualified to do so”) to near-religious esotericity, science had to open up again to survive: active proselytism was the only cure for many diseases of isolation, such as parasitic para- and pseudoscience. ■    Distributed, competitive, free-range computation was a common gateway; flourishing on unprecedented availability of computing power and big data (genomes, sky catalogs, automated experiment logs), projects tempted laypeople with participation thrill — even with a chance to find something that eluded professionals: “Grab the data, treat it (critical point!) with some filtering, biasing, massaging — all making at least some sense… run an aggregate and see it produce, often on the first try, something suggestive, or outright sensational, or even publishable.” Another wave arrived with affordable table-top experimental setups (materials, biochemistry, genetics); but the real avalanche began when feeleries and the first unchaperoned Minds entered the scene — “eager to learn and to question” like true amateurs. ■    It was a “land grab”: so many new tools, angles, eyes, so much to doubt and test; for a time, it seemed like any random shot poked a hole. The deluge of amateur science wasnt an ignorable nuisance anymore — even if much of it was inevitably junk, pseudoscience, wishful thinking; what redeemed its best specimens — apart from verifiable calculations — was freshness of outlook and “barbarian muscularity.” Sifting, checking, contextualizing the “research at large” was no after-work hobby anymore: it took decades to catch up with the flood, even now interesting finds are being unearthed from those early piles. Like another ugly duckling of open source software, folk science was trounced for retreading what “real” science had done long ago — like an embryo recapitulating its evolutionary past so it could challenge the present. ■    In the turmoil of early Change, societies survived by investing in education that soaked in surplus labor, became more per-child than ever; teaching math and scientific method slid towards ever earlier ages. Even that would likely have failed if not for the new power of seeing curable diseases in what used to be written off to innate human limitations. At the end of the day, amateur science didnt replace institutional science but formed an unprecedentedly broad base for it, the divide between “high” and “low” science getting blurrier with time; there remains a highlighted and socially stimulated progression from consumers, to interested observers, to assistants or “data sifters,” to competent researchers — potential originators of scientific waves.  ■    Naturally there was opposition: although computational modelling was then booming all across science, amateurs with their “blind” “meaningless” number crunching were a convenient target. Now as then, many fundamentally distrust data-driven research as “undigested,” deficient of a theoretical foundation (which “you cant just calculate”); to them, numeric methods — for all their practicality — are, at best, “black magic” contributing little to our understanding of the world. This line of argument never died out; the “invasion of numericity” has been singled out as an early symptom of the “asystemity cancer” — “throwing feeleries at a problem in hope that something sticks” instead of building understanding from the ground up. On the other hand, not understanding everything our models tell us isnt new: there have always been hard-to-explain, misinterpreted, undeservedly ignored results — only now they come from computations more often than experiments; computing with junk data may be wasteful but not worse than underplanned or badly executed experiments. “Understanding always grows, in breadth and in depth, yet remains in perpetual deficit: lets never see it catch up with the world to be understood.”  ■    The arrival of Minds seemed, for a time, to validate the concerns of the “we dont understand” faction. That was a great and shattering time — a catastrophe, to contemporary observers: by fresh eye more than any intellectual superiority, the first Minds revealed chasms of wishful data fitting, shaky concepts, self-taught selective blindness, strangely overlooked finds; much of the damage was in biology, psychology, medicine — basically empirical but with more leeway in interpretations than “hard sciences” can afford. And yet Minds never quite sided with the conservatives; it might have been “a distaste for sweeping pronouncements” but mostly the awareness that if computation cant produce understanding, Minds themselves — “bundles of compute” — have to forfeit any claim to intelligence.

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