This paper introduces the correlation-of-divergency coefficient, c-delta, a custom statistical measure designed to quantify the similarity of internal divergence patterns between two groups of values. Unlike conventional correlation coefficients such as Pearson or Spearman, which assess the association between paired values, c-delta evaluates whether the way values differ within one group is mirrored in another. The method involves calculating, for each value, its divergence from all other values in its group, and then comparing these patterns across the two groups (e.g., human vs machine intelligence). The coefficient is normalised by the average root mean square divergence within each group, ensuring scale invariance. Potential applications of c-delta span quantum physics, where it can compare the spread of measurement outcomes between quantum systems, as well as fields such as genetics, ecology, psychometrics, manufacturing, machine learning, and social network analysis. The measure is particularly useful for benchmarking, clustering validation, and assessing the similarity of variability structures. While c-delta is not bounded between -1 and 1 and may be sensitive to outliers (but so is PMCC), it offers a new perspective for analysing internal variability and divergence. The article discusses the mathematical formulation, potential adaptations for complex data, and the interpretative considerations relevant to this alternative approach.

翻译：本文提出发散相关性系数（c-delta），这是一种用于量化两组数值间内部发散模式相似性的定制化统计度量。与评估配对数值间关联性的传统相关系数（如Pearson或Spearman）不同，c-delta评估的是某一组数值内部的差异模式是否在另一组数值中得到镜像反映。该方法通过计算每个数值与其所在组内所有其他数值的发散度，进而比较两组（例如人类智能与机器智能）之间的发散模式。该系数通过各组内部平均均方根发散度进行归一化处理，从而确保尺度不变性。c-delta的潜在应用领域涵盖量子物理学（可用于比较量子系统间测量结果的分布差异），以及遗传学、生态学、心理测量学、制造业、机器学习和社会网络分析等多个学科。该度量特别适用于基准测试、聚类验证和变异性结构相似性评估。虽然c-delta的取值范围不受-1到1的限制，且可能对异常值敏感（但PMCC同样存在此特性），但它为分析内部变异性和发散性提供了全新视角。本文详细讨论了该方法的数学表述、针对复杂数据的潜在适应性改进，以及这种替代性分析路径所涉及的解释性考量。