Terminological framework bridges datasets, knowledge decay, and machine unlearning in digital environments, suggesting new connections.
Priority registration for the concept of Digital Information Ecology within the AUGMANITAI framework (Ehstand 2025–2026). Author ORCID: 0009-0006-3773-7796. Digital Information Ecology designates the systematic study of how information units enter, persist, mutate, interact, and decay within LLM training corpora, treated as dynamic ecosystems. Bridges currently siloed literatures on machine unlearning (Bourtoule et al. 2021), dataset documentation (Gebru et al. 2021), and temporal knowledge degradation (Cheng et al. 2024) into a unified ecological vocabulary. Full definitions maintained in the unpublished AUGMANITAI Compendium. ISO 704 / ISO 1087 / ISO 30042 compliant.
No takes yet. Share an insight, caveat, or question.
Andreas Ehstand (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: