In the context of a Natural Language Processing (NLP) research project aimed at predicting the political affiliation of authors based on their written articles, your team has access to a large and diverse dataset. The dataset includes articles from multiple authors, each with a known political affiliation. To ensure the model's ability to generalize and to prevent data leakage, you decide to follow the standard 80%-10%-10% data distribution across training, testing, and evaluation subsets. Considering the importance of maintaining the integrity of the dataset's distribution and the need for the model to learn generalizable patterns, which of the following strategies is the MOST appropriate for distributing the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion? Choose one correct option.