Contextual LLM Representations Encode Fine-Grained Semantic Transparency

Global distributional features and cross-context geometry jointly predict semantic transparency, the degree of semantic correspondence between a derived word and its morphological constituents. A stability hypothesis connects distributional accounts of morphological meaning to contextual representations, predicting that stronger constituent constraints accompany higher cross-context consensus and lower dispersion. A controlled resource combines 204 English derived forms spanning attested, novel and structurally degraded conditions, isolated-word transparency ratings, and 30 sentence contexts per item. A modelling hierarchy compares stem proximity, global distributional features and cross-context geometry across five pretrained encoder-only and decoder-only checkpoints. Global features improve on stem proximity and predict both structural contrasts and graded variation within conditions. At the preselected layers, adding contextual geometry raises within-condition random-forest R² from .12 to values between .15 and .26. Estimated increments are positive in all five checkpoints, with bootstrap intervals excluding zero for the two encoders. The additional signal is concentrated in morphologically intact forms and is strongest for novel, well-formed items in four of five checkpoints. The median aggregate increment is .26 under ridge regression and .04 under random forests, with each estimator fitted to both the global and combined feature sets. Aggregate and within-condition evaluation address complementary questions about prediction across the stimulus set and graded differences within conditions. These findings connect lexical support and contextual geometry to human semantic judgements, distinguish structural sensitivity from graded semantic accessibility, and show that predictive increments must be interpreted relative to the fitted comparison and target granularity.