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Abstract The promise of collaboration between humans and algorithms in producing good decisions is stimulating much experimentation. Drawing on research in organization design can help us to approach this experimentation systematically. I propose typologies for considering different forms of division of labor between human and algorithm as well as the learning configurations they are arranged in, as basic building blocks for this endeavor.
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Stored only when both papers are in the corpus — 126 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.