Data science and automation in the process of theorizing: Machine learning’s power of induction in the co-duction cycle
Daan Kolkman, Gwendolyn K Lee, Arjen van Witteloostuijn, Chaohai Shen · 2024
Read Data science and automation in the process of theorizing: Machine learning’s power of induction in the co-duction cycle by Daan Kolkman, Gwendolyn K Lee, Arjen van Witteloostuijn, Chaohai Shen (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11534228.txt.
Description
Kolkman, Lee, and Van Witteloostuijn argued in twenty twenty-four that machine learning can build theory, not just guess outcomes. Stock talk ties such tools to scores at the cost of sense. They set a five-step loop that joins three thought moves in one cycle.
Steps run from part picks and scans to claim builds, claim tests, and theory. A firm growth case with real data walks the loop in full. The cycle, named co-duction, gives learned reads a place in proof work. Guess strength and sense can rise as one, they show.
Catalog record
| CREATOR | Daan Kolkman, Gwendolyn K Lee, Arjen van Witteloostuijn, Chaohai Shen |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 97 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |