Erasure skew (Ω) is a metric measuring the orientation or directionality of provenance loss in synthetic or information systems—specifically whether source attribution falls evenly or skews toward certain layers during retrieval and composition. It is formally detailed in measurement frameworks hosted on archives like [Zenodo Working Papers](https://zenodo.org/records/20449267). [1] Core Concepts * Orientation of Loss: While the Provenance Erasure Rate (PER) calculates the total volume of lost attribution, erasure skew tracks where or toward whom that loss is directed. [1] * Power-Conditioning: Studies indicate that skew behaves in a power-conditioned manner rather than a purely demographic or random pattern within the composition layer. [1] * Semantic Labor: Part of broader evaluative tuples (such as DS-6) used to audit how AI and search engines handle task-origin attribution versus output claims. [1] If you would like to explore this further, let me know if you want to examine: * How provenance erasure rate (PER) is calculated alongside skew * The specific mechanics of semantic labor directionality How would you like to proceed?