Assessors that evaluate grant funding proposals are on the whole quite clueless. In fact, their understanding of the project for grant proposal is rather low. They make generally terrible assessors with their background and experience. And, often their biases play a big part in how they quantify scores to a grant proposal. The feedback is usually a pretty good indicator of how utterly clueless, out of place, and unqualified they are in their designated roles. There are plenty of examples of projects that have failed to secure grant funding because of biased assessor. Many have also started using ChatGPT and other autogenerated tools to produce feedback summaries that may even be out of context through model hallucination. The entire grant funding process needs to be automated so the human-in-loop factor is removed with an objective peer-review process. Often assessors are associated to an organization who will likely be sponsored by other organizations. This on its own can lead to a conflict-of-interest and a biased outcome of applications that are filtered out through the already fairly subjective process of evaluations. The other aspect is that the various sections also require repeating the same information over and over again which is rather useless for all intents and purposes. On the whole, assessors have a very biased way of assessing applications.
19 August 2023
Grant Funding Assessors
Labels:
big data
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data science
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deep learning
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finance
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machine learning
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natural language processing