Skriining

Skriining is a neologism describing a structured preliminary evaluation process designed to quickly filter a large set of items, people, or signals for further analysis. The term appears in discussions of data triage and rapid screening methods, sometimes as a variant spelling or a blend of screening and triaging. In practice, skriining combines criteria from multiple domains—quantitative metrics, qualitative judgment, and contextual signals—into a staged workflow. Typically, the process involves a first pass to exclude clearly irrelevant or high-risk items, followed by a more detailed assessment for those that pass, with outputs that guide whether an item proceeds to full evaluation, is put on hold, or is rejected. Decision rules are often codified as scoring rubrics or decision trees, with an emphasis on transparency and reproducibility.

Common applications include research participant recruitment, content moderation and governance, security screening, and large-scale product or idea vetting in fast-moving environments. It aims to balance speed with accuracy, reducing the cognitive load on human reviewers through automation and structured criteria, while preserving the possibility of human oversight in edge cases.

Critiques of skriining focus on potential biases introduced by composite criteria, the risk of over-filtering that excludes novel or minority cases, and the need for regular audits to prevent drift in scoring rules. Because the term is relatively new and not universally defined, implementations vary significantly across domains.