Sandcastles of hope? Civil society organizations and the working conditions of migrant farmworkers in North America

This research explores whether civil society organizations (CSOs) can contribute to more effectively regulating the working conditions of temporary migrant farmworkers in North America. This dissertation unfolds in five parts. The first part of the dissertation sets out the background context. The context includes the political economy of agriculture and temporary migrant labour more broadly. It also includes the political economy of the legal regulations that govern immigration and work relations. The second part of the research builds an analytical model for studying the operation of CSOs active in working with the migrant farmworker population. The purpose of the analytical framework is to make sense of real-world examples by providing categories for analysis and a means to get at the channels of influence that CSOs utilize to achieve their aims. To this end, the model incorporates the insights from three significant bodies of literature—regulatory studies, labour studies, and economic sociology. The third part of the dissertation suggests some key strategic issues that CSOs should consider when intervening to assist migrant farmworkers, and also proposes a series of hypotheses about how CSOs can participate in the regulatory process. The fourth part probes and extends these hypotheses by empirically investigating the operation of three CSOs that are currently active in assisting migrant farm workers in North America: the Agricultural Workers Alliance (Canada), Global Workers’ Justice Alliance (USA), and the Coalition of Immokalee Workers (USA). The fifth and final part draws together lessons from the empirical work and concluded that CSOs can fill gaps left by the waning power of actors, such as trade unions and labour inspectorates, as well as act in ways that these traditional actors can not.

Numerical Modelling of Eddy Current Probes for CANDU® Fuel Channel Inspection

Multi-frequency Eddy Current (EC) inspection with a transmit-receive probe (two horizontally offset coils) is used to monitor the Pressure Tube (PT) to Calandria Tube (CT) gap of CANDU® fuel channels. Accurate gap measurements are crucial to ensure fitness of service; however, variations in probe liftoff, PT electrical resistivity, and PT wall thickness can generate systematic measurement errors. Validated mathematical models of the EC probe are very useful for data interpretation, and may improve the gap measurement under inspection conditions where these parameters vary. As a first step, exact solutions for the electromagnetic response of a transmit-receive coil pair situated above two parallel plates separated by an air gap were developed. This model was validated against experimental data with flat-plate samples. Finite element method models revealed that this geometrical approximation could not accurately match experimental data with real tubes, so analytical solutions for the probe in a double-walled pipe (the CANDU® fuel channel geometry) were generated using the Second-Order Vector Potential (SOVP) formalism. All electromagnetic coupling coefficients arising from the probe, and the layered conductors were determined and substituted into Kirchhoff’s circuit equations for the calculation of the pickup coil signal. The flat-plate model was used as a basis for an Inverse Algorithm (IA) to simultaneously extract the relevant experimental parameters from EC data. The IA was validated over a large range of second layer plate resistivities (1.7 to 174 µΩ∙cm), plate wall thickness (~1 to 4.9 mm), probe liftoff (~2 mm to 8 mm), and plate-to plate gap (~0 mm to 13 mm). The IA achieved a relative error of less than 6% for the extracted FP resistivity and an accuracy of ±0.1 mm for the LO measurement. The IA was able to achieve a plate gap measurement with an accuracy of less than ±0.7 mm error over a ~2.4 mm to 7.5 mm probe liftoff and ±0.3 mm at nominal liftoff (2.42±0.05 mm), providing confidence in the general validity of the algorithm. This demonstrates the potential of using an analytical model to extract variable parameters that may affect the gap measurement accuracy.