Beyond the Prototypical Male Migrant

The primary theoretical accounts of migration have been largely unaffected by the feminisation of migration. But this does not mean that they are gender neutral. Drawing on the concept of gender knowledge developed by German sociologists Irene Dölling and Sünne Andresen, on the feminist critique of knowledge, feminist economics and studies on gender and migration, the paper interrogates two influential models of migration from neoclassical economics for their gendered assumptions: the Roy-Borjas selection model of migration and Jacob Mincer’s model of family migration. An analysis of their gendered assumptions about the individual, the family, the institution of the labour market and immigration policies shows that both theories explicitly and implicitly assume a male migrant as the norm and frame female migrants as passive dependents. However, the paper argues that it is not “men as such” who serve as prototypical migrants, but a specific type of white, heterosexual and middle-class masculinity, which is set as the norm while other migration realities and knowledge about the structuration of migration processes through social relations of gender, race and class are excluded. Finally, it is argued that with knowledge being a powerful site for the production of meaning in social relations, the gender knowledge in mainstream migration theories could lead to discriminatory migration policies and might also affect migrant subjectivities. This underscores the need for a more sustained dialogue between feminist and mainstream migration scholarship to further engender the field.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.