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This video tutorial focuses on evaluating annotation quality, specifically through inner annotator agreement using Cohen's kappa. It includes a case example of calculating Cohen's kappa for a small set of annotations and discusses how to interpret kappa values. The video also addresses the adjudication of disagreements to create a gold standard set of labels. It is part of a series covering the data collection pipeline from start to finish; viewers are advised to watch the preceding four videos for context. Evaluating annotation quality is crucial for justifying the contributions of a dataset, enhancing its acceptance for use in training models.