AI in education should be reframed as a relational design problem grounded in reciprocity and accountability to support learning with others and sustain communities and environments.
Information Technology for Development30(4), 626–643 (2024)
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
The stellar initial mass function (IMF), along with the star formation rate, is one of the fundamental properties that any theory of star formation must explain. An interesting feature of the IMF is that it appears to be remarkably universal across a wide range of environments. Particularly, there appears to be little variation in either the characteristic mass of the IMF or its high-mass tail between clusters with different metallicities. Previous attempts to understand this apparent independence of metallicity have not accounted for radiation feedback from high-mass protostars, which can dominate the energy balance of the gas in star-forming regions. We extend this work, showing that the fragmentation of molecular gas should depend only weakly on the amount of dust present, even when the primary heating source is radiation from massive protostars. First, we report a series of core collapse simulations using the ORION AMR code that systematically vary the dust opacity and show explicitly that this has little effect on the temperature or fragmentation of the gas. Then, we provide an analytic argument for why the IMF varies so little in observed star clusters, even as the metallicity varies by a factor of 100.
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Qualitative analysis of public comments on the FDA Food Traceability Rule identifies three tensions: data labor burdens on stakeholders, infrastructural infeasibility, and confusion from rule flexibility.
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Relational AI in Education: Reciprocity, Participatory Design, and Indigenous Worldviews
AI in education should be reframed as a relational design problem grounded in reciprocity and accountability to support learning with others and sustain communities and environments.
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"The New Era of Tech-Enabled Traceability": Tensions between the FDA's Data Governance Vision and the Lived Realities of Food Producers
Qualitative analysis of public comments on the FDA Food Traceability Rule identifies three tensions: data labor burdens on stakeholders, infrastructural infeasibility, and confusion from rule flexibility.