Broadening the Land Question in Food Sovereignty to Northern Settings: A Case Study of Occupy the Farm

Abstract

Land access is an accepted corollary to food sovereignty, long promoted by the transnational agrarian movement La Via Campesina (LVC). LVC's land access politics have evolved with increased incorporation of diverse perspectives, but remain largely focused on achieving ‘integral agrarian reform’ in the global South. Here, I take a case where food sovereignty activists (‘Occupy the Farm’ (OTF)) occupied land owned by a public university in California, the USA, in order to broaden food sovereignty's land access considerations beyond the South, and to analyze conditions where political actions (including occupations) can help achieve changes in land access regimes. The OTF action was successful in challenging cultural norms about property and achieving access, partly due to the occupation having foregrounded multiple appealing narratives that invited participation and wider support. These narratives included agroecology versus biotechnologies; community/public access versus privatization; participatory versus bureaucratic governance structure; and green space/food production versus urban development. The article tests the use of the ‘land sovereignty’ frame in expanding food sovereignty's land politics, to encompass land contestation contexts globally and deal with the particular conditions surrounding lands. The case indicates that land occupations in the North are potentially useful—but uncertain, and very context-dependent—tactics to promote land and food sovereignty.     

Latent class models for ecological inference on voters transitions

This paper introduces some new models of ecological inference within the context of estimation of voter transitions across elections. In particular, we assume that voters of a given party in a given occasion may be split into two latent types: faithful voters, who will certainly vote again for the same party and movers, who will reconsider their choice. Our models allow for unobserved heterogeneity across polling stations both in the weights of the two latent classes within each party and also when modelling the choice of unfaithful voters. Different ways of modelling the unobserved heterogeneity are considered by exploiting properties of the Dirichlet-multinomial distribution and the Brown Payne model of voting transitions can be seen as a special case within the class of models presented here. We discuss pseudo-maximum likelihood estimation and present an application to recent elections in Italy.     

Effect of measurement error and autocorrelation on the X¯ chart

Measurement error and autocorrelation often exist in quality control applications. Both have an adverse effect on the X¯ chart's performance. To counteract the undesired effect of autocorrelation, we build-up the samples with non-neighbouring items, according to the time they were produced. To counteract the undesired effect of measurement error, we measure the quality characteristic of each item of the sample several times. The chart's performance is assessed when multiple measurements are applied and the samples are built by taking one item from the production line and skipping one, two or more before selecting the next.     

On projection-based tests for directional and compositional data

A new class of nonparametric tests, based on random projections, is proposed. They can be used for several null hypotheses of practical interest, including uniformity for spherical (directional) and compositional data, sphericity of the underlying distribution and homogeneity in two-sample problems on the sphere or the simplex. The proposed procedures have a number of advantages, mostly associated with their flexibility (for example, they also work to test “partial uniformity” in a subset of the sphere), computational simplicity and ease of application even in high-dimensional cases.     

Nonlinear mixed-effects models with scale mixture of skew-normal distributions

Aiming to avoid the sensitivity in the parameters estimation due to atypical observations or skewness, we develop asymmetric nonlinear regression models with mixed-effects, which provide alternatives to the use of normal distribution and other symmetric distributions. Nonlinear models with mixed-effects are explored in several areas of knowledge, especially when data are correlated, such as longitudinal data, repeated measures and multilevel data, in particular, for their flexibility in dealing with measures of areas such as economics and pharmacokinetics. The random components of the present model are assumed to follow distributions that belong to scale mixtures of skew-normal (SMSN) distribution family, that encompasses distributions with light and heavy tails, such as skew-normal, skew-Student-t, skew-contaminated normal and skew-slash, as well as symmetrical versions of these distributions. For the parameters estimation we obtain a numerical solution via the EM algorithm and its extensions, and the Newton-Raphson algorithm. An application with pharmacokinetic data shows the superiority of the proposed models, for which the skew-contaminated normal distribution has shown to be the most adequate distribution. A brief simulation study points to good properties of the parameter vector estimators obtained by the maximum likelihood method.     

Detecting serial dependencies with the reproducibility probability autodependogram

The autodependogram is a graphical device recently proposed in the literature to analyze autodependencies. This paper proposes a normalization of this diagram taking into consideration the concept of reproducibility probability (RP). The result is a novel tool, named RP-autodependogram, which permits to study the strength and the stability of the evidence about the presence of lag-dependence. A simulation study on well-established time-series models is carried out to investigate the behavior of the RP-autodependogram also in comparison with other diagrams studying autodependencies. An application to financial data is finally considered to appreciate its usefulness in the identification of parametric/nonparametric models.     

The new strategy for the concise presentation of sampling errors in the Italian Structural Business Statistics Survey

Reporting sampling errors of survey estimates is a problem that is commonly addressed when compiling a survey report. Because of the vast number of study variables or population characteristics and of interest domains in a survey, it is almost impossible to calculate and to publish the standard errors for each statistic. A way of overcoming such problem would be to estimate indirectly the sampling errors by using generalized variance functions, which define a statistical relationship between the sampling errors and the corresponding estimates. One of the problems with this approach is that the model specification has to be consistent with a roughly constant design effect. If the design effects vary greatly across estimates, as in the case of the Business Surveys, the prediction model is not correctly specified and the least-square estimation is biased. In this paper, we show an extension of the generalized variance functions, which address the above problems, which could be used in contexts similar to those encountered in Business Surveys. The proposed method has been applied to the Italian Structural Business Statistics Survey case.     

Local Labour Markets Identification: a United Point of View*

Abstract. In this paper a unifying point of view is discussed of the methodologies and algorithms actually proposed for the identification of local labour market. The emphasis is on the main contributions to two broad methodological areas of functional regionalization and homogeneous spatial clustering. Most Italian contributions are considered in detail and some critical notes are pointed out in order to show that the methodological background is just the same for both approaches. The interpretations of the outputs of the proposed methodologies is discussed in terms of local labour markets.