Beyond the Brand: Utilizing the "Dud Index" for Academic Impact.

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Title: Beyond the Brand: Utilizing the "Dud Index" for Academic Impact.
Authors: Bell, Reginald L.1
Source: Journal of Higher Education Theory & Practice. 2026, Vol. 26 Issue 2, p1-18. 18p.
Subject Terms: False positive error, Research evaluation, Periodical articles, Gemini (Chatbot), Language models
Abstract: Google Gemini is a modern large language model (LLM) that has quickly become a "super tool for superusers.' Similar to garbage in, garbage out (GIGO) in statistics, in the hands of an expert user, Gemini is a tool indissoluble with speed and accuracy of answering important research questions. Knowing how to frame expert questions to prompt the LLM is essential. I ended up asking Gemini nine carefully crafted questions that evolved as Gemini answered each of my previous questions, respectively. By the time question #9 was answered, a "Dud Index' had been firmly established and justified. With the use of Gemini, I was able to developed a utility threshold to identify where journal "prestige' masks a lack of "utility.' This helps identify Type 1 Errors (False Positives: High rank but low impact) and Type 2 Errors (False Negatives: Low rank but high impact. Facts support the claim there is a Type 1 Error (Prestige Trap): ABDC Rank is A/A*, but the Dud Index is > 35%. Facts support a Type 2 Error (Hidden Gem): ABDC Rank is B/C, but the Dud Index is < 15%. Implications for business school deans and tenure review committees are critical and discussed regarding academic impact policies that inadvertently hurt faculty. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Higher Education Theory & Practice is the property of North American Business Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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DbLabel: Education Research Complete
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PubType: Academic Journal
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  Data: Beyond the Brand: Utilizing the &quot;Dud Index&quot; for Academic Impact.
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  Data: Google Gemini is a modern large language model (LLM) that has quickly become a &quot;super tool for superusers.&#39; Similar to garbage in, garbage out (GIGO) in statistics, in the hands of an expert user, Gemini is a tool indissoluble with speed and accuracy of answering important research questions. Knowing how to frame expert questions to prompt the LLM is essential. I ended up asking Gemini nine carefully crafted questions that evolved as Gemini answered each of my previous questions, respectively. By the time question #9 was answered, a &quot;Dud Index&#39; had been firmly established and justified. With the use of Gemini, I was able to developed a utility threshold to identify where journal &quot;prestige&#39; masks a lack of &quot;utility.&#39; This helps identify Type 1 Errors (False Positives: High rank but low impact) and Type 2 Errors (False Negatives: Low rank but high impact. Facts support the claim there is a Type 1 Error (Prestige Trap): ABDC Rank is A/A*, but the Dud Index is &gt; 35%. Facts support a Type 2 Error (Hidden Gem): ABDC Rank is B/C, but the Dud Index is &lt; 15%. Implications for business school deans and tenure review committees are critical and discussed regarding academic impact policies that inadvertently hurt faculty. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: &lt;i&gt;Copyright of Journal of Higher Education Theory &amp; Practice is the property of North American Business Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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      – SubjectFull: False positive error
        Type: general
      – SubjectFull: Research evaluation
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      – SubjectFull: Periodical articles
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      – SubjectFull: Gemini (Chatbot)
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      – SubjectFull: Language models
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              Text: 2026
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