The hypothesis was tested that children whose families differ in socioeconomic status (SES) differ in their rates of productive vocabulary development because they have different language-learning experiences. Naturalistic interaction between 33 high-SES and 30 mid-SES mothers and their 2-year-old children was recorded at 2 time points 10 weeks apart. Transcripts of these interactions provided ...

The contributions of social processes and computational processes to early lexical development were evaluated. A re-analysis and review of previous research cast doubt on the sufficiency of social approaches to word learning. An empirical investigation of the relation of social-pragmatic and data-providing features of input to the productive vocabulary of sixty-three 2-year-old children reveale...

Information Production Processes (IPPs) without low-productive sources are studied. A success-breeds-success or preferential attachment mechanism is established in which, from some point in time on, no new sources are created. Such systems are called mature systems. When time increases in mature systems the expected number of sources with a low number of items strictly decreases. An adaptation ...

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This paper reports evidence from a longitudinal study in which children's attention to shape in a laboratory task of artificial noun learning was correlated with a rate shift in noun acquisitions. Eight children were tested in the laboratory at 3-week intervals beginning when they had less than 25 nouns in their productive vocabulary (M age=17 months). Children were presented with a novel word ...

The relationship between physical capital. human capital and the distribution of turnings is examined empirically using cross-sectional microdata within SklSAs. Comprtrarive advantage in workers and a scale-of-resources effect among firms is predicted to lead to a rel~ti~)n~hip between capital and earnings. The level of earnings is found to be positively related to capital intensity and nrgntiv...

Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. Out of this mathematical impossibility emerges the question of how network data can be modeled in a way that reflects known empirical behaviors and respects basic statistical principles. We address this question by observing ...