PREreview del The Research Mind: A Multi-Dimensional Framework for Evaluating Research Quality, Productivity, and Integrity by Mitigating citation bias

STRENGTHS AND NOVEL CONTRIBUTIONS

Addressing a Critical Gap in Mentorship Selection: The paper's primary strength is its focus on the specific needs of emerging researchers. It correctly identifies that the C-Score, while effective for ranking, fails to provide the granular behavioral insights—namely, productivity expectations versus quality focus—needed for informed mentorship selection. The authors fill this gap by introducing the S-Score and Q-Score, which directly reflect research philosophy and output expectations.

Development of Novel and Granular Metrics (S-Score and Q-Score): The proposed metrics offer significant improvements over traditional measures:

• Productivity: Measures maximum average first-author publications over any three-year period. This metric provides a clear, quantitative expectation of productivity, smoothing out temporary bursts and offering a realistic view of the required output rate.

• Quality: Uses the maximum median citation count for first/last author papers over three years, specifically excluding self-citations. Using the median is a robust choice as it is less susceptible to skewing by a single outlier publication compared to mean citations, providing a more reliable measure of consistent quality and external validation.

Focus on Research Integrity and Network Effects: The framework systematically integrates research integrity through comprehensive self-citation analysis (Self-Citation and Self-Citation*). Furthermore, the introduction of network-level metrics (S-Score* and Q-Score*) extends evaluation beyond the individual to the entire collaboration ecosystem. This is crucial for revealing whether impact or productivity is derived genuinely or primarily from internal group validation, addressing concerns about citation farming and questionable research practices.

Accessibility and Practical Application (Web Application): The creation of a free, intuitive web application (theresearchmind.com/trm-app) is a major contribution, ensuring the framework's widespread adoption and accessibility to non-expert users, particularly students. The platform effectively translates complex bibliometric data into actionable decision-support information.

Compelling Use Case: The comparative case study clearly demonstrates the framework's utility. It distinguishes between quantity-focused (Dr. A, high S-Score, low Q-Score), quality-focused (Dr. B, low S-Score, high Q-Score), and balanced (Dr. C) researchers, highlighting how traditional C-Scores can be misleading for mentorship suitability.

WEAKNESSES AND AREAS FOR IMPROVEMENT

Dependence on OpenAlex Data and Author Disambiguation: While using OpenAlex is a strength due to its open nature, the reliability of the entire framework hinges on the quality and robustness of OpenAlex's author disambiguation system. If the system inaccurately groups or splits an author's works, the calculated S-Scores (which rely on accurate publication counts) and Q-Scores (which rely on accurate citation attribution) will be flawed.

Potential for Gaming the New Metrics: The authors must consider how the S-Score and Q-Score themselves might be gamed. For instance:

• S-Score: Researchers seeking a high S-Score might prioritize rapid submission of short, low-effort papers over longer, high-quality projects, even if the work is consistently low impact (as long as they maintain first-author status).

• Q-Score: While the median is robust against outliers, researchers might focus on ensuring a moderate level of citation across all papers, perhaps through strategic collaborative networks, rather than focusing purely on groundbreaking work.

Methodological Details on Exclusion Metrics (Self-Citation* and Q-Score*): The definition of the exclusion group for Self-Citation* and Q-Score* is described as "citations from top 10 group researchers". This definition needs more precise methodological justification:

• How is the "top 10 group" defined? Is it the top 10 co-authors by frequency, the top 10 most productive collaborators (by their S-Score), or the top 10 researchers in the network graph? Clarity on this definition is essential for reproducibility and understanding the scope of internal citation exclusion.

Contextualization and Field Normalization: The paper acknowledges that research environments differ, but it doesn't extensively discuss how the S-Score and Q-Score thresholds (e.g., S-Score of 10 being "unusually high," Q-Score above 50 being "better") relate to specific academic fields. Productivity rates vary drastically between theoretical fields (e.g., mathematics) and high-throughput experimental fields (e.g., biomedicine). A high S-Score in one field might be normal in another. The framework needs mechanisms or clear guidance for field normalization to ensure that comparisons between researchers in different disciplines remain valid.

Interpretation of S-Score and Mentorship: The S-Score measures either the researcher's personal productivity or their supervision of a high-productivity first author. While this is designed to reveal productivity expectations, the framework should explicitly clarify how it distinguishes between the two, as a mentor who co-authors many student papers (high S-Score via supervision) may offer a fundamentally different experience than a mentor who personally publishes 10 first-author papers a year.

Conclusion

The Research Mind framework represents a significant and necessary contribution to academic evaluation. By shifting the focus from composite ranking (C-Score) to decision-support assessment (S-Score and Q-Score), the authors successfully provide emerging researchers with the transparent, actionable metrics needed to select mentors whose philosophies align with their career goals.

The framework's integration of network analysis and integrity assessment adds considerable depth. Future work should focus on refining the field-normalization aspect and providing clearer methodological details on network exclusion criteria to maximize its robustness and utility across the academic spectrum. Overall, the paper introduces an important paradigm shift that merits widespread adoption.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.