Studying the impact of Generative Artificial Intelligence (GenAI) on scholarly publishing is a multifaceted and timely topic. The integration of GenAI into this field can influence various aspects such as the creation of content, peer review processes, and the accessibility and dissemination of scholarly work. Here are some specific research directions you might consider:

1. Quality and Integrity of Scholarly Outputs

• Authenticity and Originality: Investigate how GenAI can affect the originality of scholarly content, considering the potential for producing derivative works that may not easily be attributable to human authors.
• Plagiarism and Duplication: Study the challenges in detecting plagiarism when GenAI tools are capable of generating novel content that may not match existing databases.

2. Peer Review

• Automation of Peer Review: Explore the feasibility and effectiveness of using GenAI in automating parts of the peer review process, such as initial manuscript screening or checking for statistical errors.
• Bias and Fairness: Assess whether GenAI systems introduce or mitigate biases in the peer review process, compared to human reviewers.

3. Access and Inclusivity

• Language Translation: Examine how GenAI can enhance the accessibility of scholarly articles through high-quality translation services, making them available to a wider, global audience.
• Accessibility Features: Research the development of GenAI tools that improve the accessibility of scholarly content for people with disabilities, e.g., generating accessible content formats automatically.

4. Productivity and Efficiency

• Speed of Publication: Analyze the impact of GenAI on reducing the time from submission to publication, potentially transforming the current dynamics of publishing.
• Research Assistance: Study how GenAI tools serve as research assistants, helping scholars to synthesize existing literature, generate hypotheses, or even design experiments.

5. Ethical and Legal Considerations

• Authorship and Contribution: Delve into the ethical implications of using GenAI in research generation, such as defining authorship and intellectual property rights.
• Regulatory Frameworks: Identify the need for new policies or modifications to existing guidelines to govern the use of GenAI in scholarly publishing.

6. Economic Impact

• Cost Structures: Investigate how GenAI might alter the economic landscape of scholarly publishing, potentially reducing costs associated with human labor or changing the subscription and open access models.
• Market Dynamics: Look into how GenAI could affect the competitive dynamics among publishers, especially regarding the adoption of new technologies.

7. Long-Term Implications

• Scholarly Record: Consider the long-term implications of GenAI on the integrity of the scholarly record, including issues related to the preservation and archiving of AI-generated content.
• Changing Roles: Explore how roles within the academic publishing industry might evolve in response to GenAI technologies, including the skills professionals need to adapt.

Methodologies to Consider

• Qualitative Interviews: Gather insights from various stakeholders in the scholarly publishing ecosystem, such as authors, reviewers, editors, and publishers.
• Quantitative Analysis: Use statistical methods to analyze data on submission, acceptance rates, and publication times pre- and post-GenAI implementation.
• Case Studies: Conduct detailed case studies of specific journals or fields that have integrated GenAI tools into their workflows.

Potential Data Sources

• Publishing Data: Access data from publishers regarding submission logs, review times, and publication outcomes.
• Surveys: Design and distribute surveys to academics and publishing professionals to gather subjective and qualitative data.

This research will not only help in understanding the current impact of GenAI on scholarly publishing but also guide the development of best practices and policies for its future use.