Unwriting food labels: discursive challenges in the regulation of package claims

This article examines the challenges resulting from the regulation of written discourse on food packages. It uses as a case study Hong Kong’s strict new food-labeling law that requires distributers and retailers to remove certain nutritional claims from packages of imported food before they sell them. This practice of redacting unlawful text on packages requires that distributers and retailers engage in complex processes of discursive reasoning, and it some- times results in packages that are difficult for customers to interpret. The case study highlights important issues in the regulation of commercial texts concerning collaboration, intertextuality, and the conflicts that can arise when the principals, authors, and animators of such texts have different agendas.

abctools: an R package for tuning approximate Bayesian computation analyses

Approximate Bayesian computation (ABC) is a popular family of algorithms which perform approximate parameter inference when numerical evaluation of the likelihood function is not possible but data can be simulated from the model. They return a sample of parameter values which produce simulations close to the observed dataset. A standard approach is to reduce the simulated and observed datasets to vectors of summary statistics and accept when the difference between these is below a specified threshold. ABC can also be adapted to perform model choice. In this article, we present a new software package for R, abctools which provides methods for tuning ABC algorithms. This includes recent dimension reduction algorithms to tune the choice of summary statistics, and coverage methods to tune the choice of threshold. We provide several illustrations of these routines on applications taken from the ABC literature.