AI evidence extraction

Evolving Epistemic Infrastructure: The Role of Scientific Journals in the Age of Generative AI

Youngjin Yoo · Journal of the Association for Information Systems · 2024

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

11.60

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.17705/1jais.00870

Methodology & findings

Study design

Opinion piece/hermeneutic analysis - The paper is an argumentative essay examining implications of generative AI for scientific journals through conceptual analysis rather than empirical investigation..

Main result

The paper identifies that "generative AI can play is facilitating 'long jumps' in our knowledge exploration process" and proposes that "decentralization and deferred and temporary binding as two crucial characteristics of the evolving epistemic infrastructure that supports precarious knowledge production." The author argues that scientific journals are undergoing transformation due to large language models.

Research paradigm

Interpretivist/critical epistemology

Author conclusions

The author concludes that "scientific journals extend beyond their traditional gatekeeping roles" and "call[s] for scholars—as authors, reviewers, and mentors—to utilize these technologies to traverse the broad landscape of potential knowledge, fostering a more inclusive and dynamic scientific ecosystem."

Limitations

The paper provides no explicit statement of limitations
As an opinion piece focused on theoretical implications rather than empirical measurement, it does not report empirical limitations in the traditional sense.

Open questions raised

The paper identifies the need to understand how scientific journals should adapt their gatekeeping functions and infrastructure in response to generative AI, and calls for exploration of how scholars can better utilize these technologies in their various roles.
The paper identifies the need to understand how scientific journals should evolve in response to generative AI and emphasizes the importance of comprehending "the operational mechanisms of these models and the fundamentally recombinatorial nature of human knowledge creation."
The paper identifies the need to understand how scientific journals should evolve in response to generative AI and calls for new approaches to epistemic infrastructure that move beyond traditional gatekeeping functions toward more decentralized and dynamic knowledge production systems.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 79%

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