Learning to trade ethically: Knowledgeable capitalism, retailers and contested commodity chains

This paper explores what happens when corporations engage explicitly in practices of organisational learning not only to become better capitalists by generating ever more innovative ways of maintaining profitability, improving competitiveness and maximising shareholder value, but also to become more responsible corporate citizens in their business practices. In particular, I evaluate the ways in which UK food and clothing retailers are learning to develop their ethical trading programmes in response to political calls for more responsible trading. Thrift’s [Thrift, N., 2005. Knowing Capitalism. Sage, London] notion of ‘knowledgeable, or soft, capitalism’ is adopted to understand the creative and experimental ways in which retailers and their mentors (ethical consultancies, social auditors and multi-stakeholder organisations) are learning to trade ethically. Two specific examples of formal learning spaces experienced by UK food and clothing retailers are examined: (i) training courses on social auditing and (ii) corporate awareness-raising courses on ethical trade. These courses are shown to encompass various participative and affective practices of learning. And while particular limits to the success of these courses are argued to exist, ethical learning practices discussed in this paper are nonetheless suggested to play a role in the making of new, albeit moderate, forms of responsible capitalism.     

UK seismology – 2002 and beyond

Alex Brisbourne reports on a one-day discussion meeting organized by SEIS-UK for seismologists to discuss current seismological research undertaken in the UK and to consider how the provision of new seismic equipment and training facility may enhance that work.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

A re-examination of petrogenesis and 40Ar/39Ar systematics in the Chain of Ponds K-feldspar: “diffusion domain” archetype versus polyphase hygrochronology

K-feldspar (Kfs) from the Chain of Ponds Pluton (CPP) is the archetypal reference material, upon which thermochronological modeling of Ar diffusion in discrete “domains” was founded. We re-examine the CPP Kfs using cathodoluminescence and back-scattered electron imaging, transmission electron microscopy, and electron probe microanalysis. ⁴⁰Ar/³⁹Ar stepwise heating experiments on different sieve fractions, and on handpicked and unpicked aliquots, are compared. Our results reproduce the staircase-shaped age spectrum and the Arrhenius trajectory of the literature sample, confirming that samples collected from the same locality have an identical Ar isotope record. Even the most pristine-looking Kfs from the CPP contains successive generations of secondary, metasomatic/retrograde mineral replacements that post-date magmatic crystallization. These chemically and chronologically distinct phases are responsible for its staircase-shaped age spectra, which are modified by handpicking. While genuine within-grain diffusion gradients are not ruled out by these data, this study demonstrates that the most important control on staircase-shaped age spectra is the simultaneous presence of heterochemical, diachronous post-magmatic mineral growth. At least five distinct mineral species were identified in the Kfs separate, three of which can be traced to external fluids interacting with the CPP in a chemically open system. Sieve fractions have size-shifted Arrhenius trajectories, negating the existence of the smallest “diffusion domains.” Heterochemical phases also play an important role in producing nonlinear trajectories. In vacuo degassing rates recovered from Arrhenius plots are neither related to true Fick’s Law diffusion nor to the staircase shape of the age spectra. The CPP Kfs used to define the “diffusion domain” model demonstrates the predominance of metasomatic alteration by hydrothermal fluids and recrystallization in establishing the natural Ar distribution among different coexisting phases that gives rise to the staircase-shaped age spectrum. Microbeam imaging of textures is as essential for ⁴⁰Ar/³⁹Ar hygrochronology as it is for U–Pb geochronology.     

Use of LIDAR in landslide investigations: a review

This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDAR-derived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.