Structural inquiry, human agency and the contribution of Harre and Bhaskar : a case study of Wright’s “Classes”

This paper examines the contribution of aspects of critical and referential realism to the “logic” of structural explanation through an analysis of Erik Olin Wright’s Classes and the debate surrounding this work. Wright’s Classes has been selected as a case study because it offers an opportunity to examine issues pertaining to “objective” and “subjective” determinations of class and related questions of agency and structure at the level of actual methodological strategies. A close examination of the structure of Wright’s inquiry reveals a number of places where Harre’s and Bhaskar’s approaches may contribute to the prescription of methodological strategies which could overcome some of the antinomies on which the debate on Classes is based. As a case study, the paper underlines the important “underlabourer” role of critical and referential realism and their contribution to questions of agency and structure in the context of actual stages involved in structural explanation

The impact of location on contribution schedule lot entitlements

Should the owner of a penthouse unit pay more in body corporate levies than the ground floor unit owner? A decision of the Queensland Court of Appeal (McPherson JA, Chesterman and Atkinson JJ) will be of great interest to those seeking to challenge contribution schedule lot entitlements imposed under the Body Corporate and Community Management Act 1997 (Qld) (‘the Act’). The decision is Fischer v Body Corporate for Centrepoint Community Title Scheme 7779 [2004] QCA 214.

Managing wetlands sustainably as ecosystems : The contribution of the law (part 1)

The ways in which a society set standards of behaviour and of conduct for its members vary hugely. For example, accepted practices, recognised customs, spiritually or morally inspired norms, judicially declared rules, executively formulated edicts, formal legislative enactments or constitutionally embedded rights and duties. Whatever form they assume, these standards are the artificial construction of the human mind. Accordingly the law - whatever its form - can do no more and no less than regulate or set standards for human behaviour, human conduct, and human decision-making. The law cannot regulate the environment. It can only regulate human activities that impact directly or indirectly upon the environment. This applies as much to wetlands as components of the environment as it does to any other components of the environment or the environment at large. The capacity of the law to protect the environment and therefore wetlands is thus totally dependent upon the capacity of the law to regulate human behaviour, human conduct and human decision-making. At the same time the law needs to reflect the specific nature, functions and locations of wetlands. A wetland is an ecosystem by itself; it comprises a range of ecosystems within it; and it is part of a wider set of ecosystems. Hence, the significant ecological functions performed by wetlands. Then there are the benefits flowing to humans from wetlands. These may be social, economic, cultural, aesthetic, or a combination of some or of all of these. It is a challenge for a society acting through its legal system to find the appropriate balance between these ecological and these human values. But that is what sustainability requires.The ways in which a society set standards of behaviour and of conduct for its members vary hugely. For example, accepted practices, recognised customs, spiritually or morally inspired norms, judicially declared rules, executively formulated edicts, formal legislative enactments or constitutionally embedded rights and duties. Whatever form they assume, these standards are the artificial construction of the human mind. Accordingly the law - whatever its form - can do no more and no less than regulate or set standards for human behaviour, human conduct, and human decision-making. The law cannot regulate the environment. It can only regulate human activities that impact directly or indirectly upon the environment. This applies as much to wetlands as components of the environment as it does to any other components of the environment or the environment at large. The capacity of the law to protect the environment and therefore wetlands is thus totally dependent upon the capacity of the law to regulate human behaviour, human conduct and human decision-making. At the same time the law needs to reflect the specific nature, functions and locations of wetlands. A wetland is an ecosystem by itself; it comprises a range of ecosystems within it; and it is part of a wider set of ecosystems. Hence, the significant ecological functions performed by wetlands. Then there are the benefits flowing to humans from wetlands. These may be social, economic, cultural, aesthetic, or a combination of some or of all of these. It is a challenge for a society acting through its legal system to find the appropriate balance between these ecological and these human values. But that is what sustainability requires.

The contribution of maize cropping in the Midwest USA to global warming: A regional estimate

Agricultural soils emit about 50% of the global flux of N2O attributable to human influence, mostly in response to nitrogen fertilizer use. Recent evidence that the relationship between N2O fluxes and N-fertilizer additions to cereal maize are non-linear provides an opportunity to estimate regional N2O fluxes based on estimates of N application rates rather than as a simple percentage of N inputs as used by the Intergovernmental Panel on Climate Change (IPCC). We combined a simple empirical model of N2O production with the SOCRATES soil carbon dynamics model to estimate N2O and other sources of Global Warming Potential (GWP) from cereal maize across 19,000 cropland polygons in the North Central Region (NCR) of the US over the period 1964–2005. Results indicate that the loading of greenhouse gases to the atmosphere from cereal maize production in the NCR was 1.7 Gt CO2e, with an average 268 t CO2e produced per tonne of grain. From 1970 until 2005, GHG emissions per unit product declined on average by 2.8 t CO2e ha−1 annum−1, coinciding with a stabilisation in N application rate and consistent increases in grain yield from the mid-1970’s. Nitrous oxide production from N fertilizer inputs represented 59% of these emissions, soil C decline (0–30 cm) represented 11% of total emissions, with the remaining 30% (517 Mt) from the combustion of fuel associated with farm operations. Of the 126 Mt of N fertilizer applied to cereal maize from 1964 to 2005, we estimate that 2.2 Mt N was emitted as N2O when using a non-linear response model, equivalent to 1.75% of the applied N.

Managing Wetlands Sustainably as Ecosystems: The Contribution of the Law (Part 2)

In this Part 2 attention is turned towards the legal arrangements in nation states for managing wetlands. These national arrangements have effect within the international arrangements already mentioned and any regional arrangements that are relevant. However, each national system is a reflection of its own historical, cultural, political and constitutional background. It is the purpose of this Part 2 to review and assess the national approaches to wetlands management. This involves an analysis of a range of instruments. These are: constitutional rules; strategic rules; regulatory rules; and management rules. Each of these sets of rules performs different functions, assumes different forms and is differentially capable of enforcement.

Boosting the margin: A new explanation for the effectiveness of voting methods

One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Exponentiated gradient algorithms for large-margin structured classification

We consider the problem of structured classification, where the task is to predict a label y from an input x, and y has meaningful internal structure. Our framework includes supervised training of Markov random fields and weighted context-free grammars as special cases. We describe an algorithm that solves the large-margin optimization problem defined in [12], using an exponential-family (Gibbs distribution) representation of structured objects. The algorithm is efficient—even in cases where the number of labels y is exponential in size—provided that certain expectations under Gibbs distributions can be calculated efficiently. The method for structured labels relies on a more general result, specifically the application of exponentiated gradient updates [7, 8] to quadratic programs.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.