Meta-regulation approach of law : a potential legal strategy to develop socially responsible business self-regulation in least developed common law countries

In the corporate regulation landscape, 'meta-regulation' is a comparatively new legal approach. The sketchy role of state promulgated authoritative laws in pluralized society and scepticism in corporate self-regulation's role have resulted in the development of this legal approach. It has opened up possibilities to synthesize corporate governance to add social values in corporate self-regulation. The core of this approach is the fusion of responsive and reflexive legal strategies to combine regulators and regulatees for reaching a particular goal. This paper argues that it is a potential strategy that can be successfully deployed to develop a socially responsible corporate culture for the business enterprises, so that they will be able to acquire social, environmental and ethical values in their self-regulation sustainably. Taking Bangladeshi corporate laws as an instance, this paper also evaluates the scope of incorporating this approach in laws of the least developed common law countries in general.

Corporate governance as social responsibility : a meta-regulation approach to raise social responsibility of corporate governance in a weak economy

Corporate governance (CG) denotes the rules of business decision-making and directs the internal mechanism of companies to follow the output of the rules. It includes the customs, policies, laws and institutions as a set of processes that affects the way in which a corporation is directed, administered or controlled.

Meta-regulation approach to CSR

The convergence of corporate social responsibility and corporate governance has changed the mechanism of corporate accountability, which has developed "corporate self-regulation...

Legal Regulation of Corporate Social Responsibility : A Meta-Regulation Approach of Law for Raising CSR in a Weak Economy

"Even though Corporate Social Responsibility (CSR) has become a widely accepted concept promoted by different stakeholders, business corporations' internal strategies, known as corporate self-regulation in most of the weak economies, respond poorly to this responsibility. Major laws relating to corporate regulation and responsibilities of these economies do not possess adequate ongoing influence to insist on corporate self-regulation to create a socially responsible corporate culture. This book describes how the laws relating to CSR could contribute to the inclusion of CSR principles at the core of the corporate self-regulation of these economies in general, without being intrusive in normal business practice. It formulates a meta-regulation approach to law, particularly by converging patterns of private ordering and state control in contemporary corporate law from the perspective of a weak economy. It proposes that this approach is suitable for alleviating regulators' limited access to information and expertise, inherent limitations of prescriptive rules, ensuring corporate commitment, and enhance the self-regulatory capacity of companies. This book describes various meta-regulation strategies for laws to link social values to economic incentives and disincentives, and to indirectly influence companies to incorporate CSR principles at the core of their self-regulation strategies. It investigates this phenomenon using Bangladesh as a case study."--publisher website

Harnessing SD and CSR within corporate self-regulation of weak economies : a meta-regulation approach

The semantic of the terms “sustainable development” and “corporate social responsibility” have changed over time to a point where these concepts have become two interrelated processes for ensuring the far-reaching development of society. Their convergence has given dimension to the environmental and corporate regulation mechanisms in strong economies. This article deals with the question of how the ethos of this convergence could be incorporated into the self-regulation of businesses in weak economies where nonlegal drivers are either inadequate or inefficient. It proposes that the policies for this incorporation should be based on the precepts of meta-regulation that have the potential to hold force majeure, economic incentives, and assistance-related strategies to reach an objective from the perspective of weak economies.

Meta-regulation approach to raise social accountability In global supply firms

This Article proposes a meta-regulation approach to address the gap between the objectives, commitment, practice and outcome in the accountability practice of the global supply chain in the developing countries. The literatures on the accountability practice in the global supply chains typically focuses on the strategies for raising corporate social accountability standards in multinational buying firms and seldom focuses on this strategies in the outsourced firms in the developing countries. This article tries to fill this void by examining the situation in Bangladesh, the third largest RMG supply country in the world. It conceptualizes a meta-regulation approach with the aim of raising social accountability practice in this industry. It shows that this regulation approach is suitable to effectively raise this practice standard in a perspective where the non-legal drivers are meagrely low, global buying firms are highly profit driven and the governmental agencies are either inadequate or highly corrupt.

Network pricing for QoS: A 'Regulation' approach

V. S. Borkar’s work was supported in part by grant number III.5(157)/99-ET from the Department of Science and Technology, Government of India. D. Manjunath’s work was supported in part by grant number 1(1)/2004-E-Infra from the Ministry of Information Technology, Government of India.

A meta-analytic approach to quantifying scientific uncertainty in stock assessments

Quantifying scientific uncertainty when setting total allowable catch limits for fish stocks is a major challenge, but it is a requirement in the United States since changes to national fisheries legislation. Multiple sources of error are readily identifiable, including estimation error, model specification error, forecast error, and errors associated with the definition and estimation of reference points. Our focus here, however, is to quantify the influence of estimation error and model specification error on assessment outcomes. These are fundamental sources of uncertainty in developing scientific advice concerning appropriate catch levels and although a study of these two factors may not be inclusive, it is feasible with available information. For data-rich stock assessments conducted on the U.S. west coast we report approximate coefficients of variation in terminal biomass estimates from assessments based on inversion of the assessment of the model’s Hessian matrix (i.e., the asymptotic standard error). To summarize variation “among” stock assessments, as a proxy for model specification error, we characterize variation among multiple historical assessments of the same stock. Results indicate that for 17 groundfish and coastal pelagic species, the mean coefficient of variation of terminal biomass is 18%. In contrast, the coefficient of variation ascribable to model specification error (i.e., pooled among-assessment variation) is 37%. We show that if a precautionary probability of overfishing equal to 0.40 is adopted by managers, and only model specification error is considered, a 9% reduction in the overfishing catch level is indicated.

A meta-heuristic approach to parallel code generation

The efficient generation of parallel code for multi-processor environments, is a large and complicated issue. Attempts to address this problem have always resulted in significant input from users. Because of constraints on user knowledge and time, the automation of the process is a promising and practically important research area. In recent years heuristic approaches have been used to capture available knowledge and make it available for the parallelisation process. Here, the introduction of a novel approach of neural network techniques is combined with an expert system technique to enhance the availability of knowledge to aid in the automatic generation of parallel code.

Meta-analysis: a unifying meta-likelihood approach framing unobserved heterogeneity, study covariates, publication bias, and study quality

OBJECTIVES: This contribution provides a unifying concept for meta-analysis integrating the handling of unobserved heterogeneity, study covariates, publication bias and study quality. It is important to consider these issues simultaneously to avoid the occurrence of artifacts, and a method for doing so is suggested here. METHODS: The approach is based upon the meta-likelihood in combination with a general linear nonparametric mixed model, which lays the ground for all inferential conclusions suggested here. RESULTS: The concept is illustrated at hand of a meta-analysis investigating the relationship of hormone replacement therapy and breast cancer. The phenomenon of interest has been investigated in many studies for a considerable time and different results were reported. In 1992 a meta-analysis by Sillero-Arenas et al. concluded a small, but significant overall effect of 1.06 on the relative risk scale. Using the meta-likelihood approach it is demonstrated here that this meta-analysis is due to considerable unobserved heterogeneity. Furthermore, it is shown that new methods are available to model this heterogeneity successfully. It is argued further to include available study covariates to explain this heterogeneity in the meta-analysis at hand. CONCLUSIONS: The topic of HRT and breast cancer has again very recently become an issue of public debate, when results of a large trial investigating the health effects of hormone replacement therapy were published indicating an increased risk for breast cancer (risk ratio of 1.26). Using an adequate regression model in the previously published meta-analysis an adjusted estimate of effect of 1.14 can be given which is considerably higher than the one published in the meta-analysis of Sillero-Arenas et al. In summary, it is hoped that the method suggested here contributes further to a good meta-analytic practice in public health and clinical disciplines.

A meta-learning approach to automatic kernel selection for support vector machines

Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings.