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So, we have the admittedly daunting task of imagining alternatives to these vague and technocratic redefinitions of human learning and intelligence, to dream up alternatives that are reciprocal, humane, and open to diversity. Can we imagine a personal, poetic, and situated model in which our data systems are not pre-trained with biases originating from the monocultures of Silicon Valley, but which are open to our environment and our needs, and to be influenced and understood by us? Can we also imagine systems of data that are not structured solely by the industrial values of automation and efficiency, but also can encompass the productive inefficiencies, impossible-to-valuate social exchanges, and serendipitous connections that are essential to the quality, not just quantities, of our lives and work.

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