Transforming literary art like short fiction into an accurate dataset requires critical thinking, because every annotation decision determines how a story will be represented and analyzed later.
Abiba Moncriffe, a sophomore African American Studies major at Howard University, initially struggled to annotate settings during her first week because she “did not have a lot of information about where the characters were.”
She explains how that uncertainty forced her to sit with the absence of place, and “taught me the importance of details in storytelling and how the missing elements of location added to the way the story read.”India Crowe, a sophomore Sociology major at Howard, admitted that she often debates whether to include characters who “appear only once,” but she realized that “these moments teach me that less-involved characters still contribute to world-building and serve a purpose, even if it is not as explicit as others.”
These decisions require judgment. Data Rangers follow a shared system, rely on context clues, and ask questions when something is unclear. Their attention to detail demonstrates intellectual discipline and safeguards the accuracy of the dataset.
Each careful annotation reminds us that building a dataset from short fiction is an act of interpretation, not automation.
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