Modeling distributional stability in learner English passives: The Linguistic Clusters Framework

Frequency-based learner corpus approaches struggle to separate systematic grammatical patterning from artifacts of prompts or register. This study introduces the Linguistic Clusters (LC) Framework, a multi-metric procedure for identifying stable distributional patterns in learner passives. The framework implements a three-layer architecture integrating instance-level filters, schema-level acceptance criteria, and validation procedures. Applied to over 57,000 valid passive constructions from the International Corpus of Learner English, the LC Framework identified eight validated schemas from argumentative essays across diverse first-language backgrounds. Permutation testing confirmed non-random TAM × complement structure and demonstrated that schema acceptance is structurally rather than lexically driven. Sensitivity analysis showed that the majority of schemas remain stable across threshold variations, while ablation analysis attributed most false-positive reduction to instance-level filters. The validated schemas cluster around modal passives with spatial prepositional complements, revealing a frequency–formulaicity dissociation: modal-be passives account for a small fraction of corpus constructions yet comprise the majority of validated schemas. These findings support usage-based accounts that treat L2 constructional development as driven by the convergence of multiple distributional cues, not by raw token frequency alone.