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Chronicles

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A profile of iNaturalist, a not-for-profit social network that uses an ML algorithm to help users identify plants and animals, driving cooperation and consensus

A not-for-profit initiative of the California Academy of Sciences and the National Geographic Society, iNaturalist says it aims … Tweets: @berkeleyischool , @kingjen , @amy_harmon , @ucdavisccs , @nytscience , @femoraleshells , and @echinoblog Tweets: @berkeleyischool : 🏞️ Alum @kueda co-founded @inaturalist back in 2008 as a MIMS Final Project project. Now, @nytimes is talking about the platform, its meteoric rise in popularity, and how it teaches people to get along. Read more — https://www.nytimes.com/... https://twitter.com/... @kingjen : One of the best examples of @BerkeleyISchool master's projects ever. Highlights what is best about the iSchool. https://twitter.com/... Amy Harmon / @amy_harmon : You could leave Twitter for Mastodon, or you could leave it for Post. But don't say I didn't tell you about @inaturalist. https://www.nytimes.com/... Davis CitSci / @ucdavisccs : Fans (including us!) of the #CitizenScience app & website @inaturalist will rejoice to read how this collaborative platform brings people together over ID-ing creatures big & small. It's an example for positive online communication in social networks. https://www.nytimes.com/... @nytscience : Can a website cataloging the known world of plant and animal life on Earth teach humans how to talk to each other again? Fans of iNaturalist are trying to find out. https://www.nytimes.com/... @femoraleshells : iNaturalist “A not-for-profit initiative of the California Academy of Sciences and the National Geographic Society, iNaturalist says it aims to connect people to nature through technology."https://www.nytimes.com/ ... https://twitter.com/... @echinoblog : I'm happy to see @inaturalist as a good shared space and one where different people can meet & build something-its neither idyllic nor a perfect experience for any number of reasons. But if natural history gets one in the “plus” column then good! https://www.nytimes.com/...

New York Times Amy Harmon

Context & Ripple Effects

iNaturalist arrives at a moment when the citizen-science platform model is already proven: eBird has become the global default for logging bird sightings since 2002, and [[a:942172|Pl@ntNet demonstrated that collaborative plant identification works without an engagement imperative]]. What distinguishes iNaturalist is its institutional backing — it is a not-for-profit joint initiative of the California Academy of Sciences and the National Geographic Society, co-founded in 2008 as a Berkeley MIMS final project.

The timing matters because the commercial social web is losing its scientific user base: researchers who relied on Twitter for science communication during the pandemic are now leaving the service, per related coverage. A profile of a cooperative, ML-assisted species-ID network lands as a working alternative model.

First-order effects

  • Users get algorithmic species identification plus community consensus on their observations, while the California Academy of Sciences and National Geographic Society gain a growing, structured biodiversity dataset under not-for-profit governance.

Second-order effects

  • As iNaturalist's public observation maps grow, it inherits the same exposure problem other biodiversity databases hit when they had to rebuild infrastructure to hide endangered-species locations from poachers — location privacy becomes an engineering requirement, not an afterthought.

Third-order effects

  • If scientists continue migrating off commercial platforms, not-for-profit networks like iNaturalist, eBird, and Pl@ntNet consolidate into the de facto infrastructure for field biology — shifting moderation, data stewardship, and anti-poaching safeguards onto academic institutions rather than ad-funded companies.

The trend: Citizen-science networks are absorbing the role commercial social media played for naturalists and researchers, trading engagement metrics for curated, institution-governed observation data.