Brave new brains: sociology,family and the politics of knowledge

This article critically explores sociological arguments for greater biosocial synthesis, centring contemporary developments in public policy to demonstrate how such a reframing of humanity tends to reinforce existing political orders and socially patterned normativities. The case for further amalgamation of the social and life sciences is examined to suggest that production of somatic markers of truth from relational encounters largely relies upon an anaemic and politically contained version of the social as acquired in early childhood. More specifically, the gendered, classed and culturally specific practice of parenting children has come to occupy a new significance in accounts of social brains and environmentally reactive genomes. This is highlighted through a discussion of ‘early intervention’ as a heavily biologized policy rationale framing opportunities for biosocial collaboration. It is argued that late capitalist objectives of personal investment and optimization are driving this assimilation of the social and life sciences, pursuing an agenda that traces and re‐scores long‐standing social divisions in the name of progress.     

Relabelling in Bayesian mixture models by pivotal units

Label switching is a well-known and fundamental problem in Bayesian estimation of finite mixture models. It arises when exploring complex posterior distributions by Markov Chain Monte Carlo (MCMC) algorithms, because the likelihood of the model is invariant to the relabelling of mixture components. If the MCMC sampler randomly switches labels, then it is unsuitable for exploring the posterior distributions for component-related parameters. In this paper, a new procedure based on the post-MCMC relabelling of the chains is proposed. The main idea of the method is to perform a clustering technique on the similarity matrix, obtained through the MCMC sample, whose elements are the probabilities that any two units in the observed sample are drawn from the same component. Although it cannot be generalized to any situation, it may be handy in many applications because of its simplicity and very low computational burden.     

A note on empirical likelihoods derived from pairwise score functions

Pairwise likelihood functions are convenient surrogates for the ordinary likelihood, useful when the latter is too difficult or even impractical to compute. One drawback of pairwise likelihood inference is that, for a multidimensional parameter of interest, the pairwise likelihood analogue of the likelihood ratio statistic does not have the standard chi-square asymptotic distribution. Invoking the theory of unbiased estimating functions, this paper proposes and discusses a computationally and theoretically attractive approach based on the derivation of empirical likelihood functions from the pairwise scores. This approach produces alternatives to the pairwise likelihood ratio statistic, which allow reference to the usual asymptotic chi-square distribution and which are useful when the elements of the Godambe information are troublesome to evaluate or in the presence of large data sets with relative small sample sizes. Two Monte Carlo studies are performed in order to assess the finite-sample performance of the proposed empirical pairwise likelihoods.     

Some Relationships Between Skew-Normal Distributions and Order Statistics from Exchangeable Normal Random Vectors

The article explores the relationship between distributions of order statistics from random vectors with exchangeable normal distributions and several skewed generalizations of the normal distribution. In particular, we show that the order statistics of exchangeable normal observations have closed skew-normal distributions, and that the corresponding density function is log-concave when the order statistic is extreme. Special attention is given to the bivariate case, which is related to the univariate skew-normal distribution. The applications discussed focus on the lifetimes of coherent systems.     

Occupations and industries of employment of Māori university graduates: early career aspirations and destinations

ABSTRACT

Technological advancements, a changing demography, and labour market demands mean that it is increasingly important to have a skilled Māori labour workforce. Māori students are graduating from universities in increasing numbers but little is known about their careers post-graduation. Our aim was to describe the early career aspirations and destinations of Māori university graduates in the Graduate Longitudinal Study New Zealand. Data were collected when participants were in their final year of study (n?=?626) and 2 years post-graduation (n?=?455). The most common occupation types 2 years post-graduation were professionals (66%), clerical or administrative workers (10%), and managers (9%). The most common industries of employment were education and training (29%), health care and social assistance (22%), and legal and accounting services (8%). The percentage of graduates who, in their final year of study, planned to enter into industries that matched their actual industries at 2 years post-graduation was 67%. There were some differences across fields of study, with Health and Education graduates working in industries more closely aligned with their studies than Science or Humanities graduates. Future surveys will enable us to track graduates to see if these patterns change as graduates settle into their careers post-graduation.