Beyond the Brand: Utilizing the “Dud Index” for Academic Impact
DOI:
https://doi.org/10.33423/5fspy681Keywords:
higher education, artificial intelligence, Dud Index, Gatekeeper Effect, Gemini, h-index, High-Ranked Graveyards, Hidden Gem, legacy journal skew, Matthew Effect, paywall penalty, Prestige Trap, ranks, Type 1 Error, Type 2 Error, Uncitedness Ratios, Utility ThresholdAbstract
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.
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