Abstract This article presents an exploration of the usage and distribution of the 1-handshape classifier (Cl-1) and the 2-handshape classifier (Cl-2), both diachronically and synchronically, tracking classifier development in a detailed dataset of Nicaraguan individual homesigners and two groups of Nicaraguan Sign Language signers. Both phonological and semantic dimensions are analyzed in service of discerning what factors impact the choice and phonological production of the relevant classifiers. Phonologically we find that the distribution of Cl-1 and Cl-2 is quite different than would be expected were articulatory complexity the main motivating factor. We suggest also that this dataset is a microcosm of the system as a whole, and patterns of 1-handed and 2-handed signs vary across groups. Semantically, we find that (1) classifier use is more semantically restricted among individual homesigners. (2) As classifiers become more semantically frequent and flexible in the earliest cohort of Nicaraguan signers, constraints on production emerge. (3) Vertical transmission of those classifiers and constraints leads to more flexible usage in subsequent cohorts.

Employment in STEM occupations suffered smaller peak-to-trough percentage declines than non-STEM occupations during the Great Recession and COVID-19 recession, suggesting a relative resiliency of STEM employment.We exploit the sudden peak-to-trough declines in STEM and non-STEM employment during the COVID-19 recession to measure STEM recession-resiliency, decomposing our difference-in-differences estimate into parts explained by various sources.We find that STEM knowledge importance on the job explains the greatest share of STEM employment resiliency, and that workers in non-STEM occupations who nonetheless use STEM knowledge experienced better employment outcomes.STEM employment resiliency may explain the mild effects of COVID-19 on innovative activity.