Better journalism or better profits? A key convergence issue in an age of concentrated ownership

Convergence has become an accepted form of journalism at media organisations around the world. These organisations are adopting a range of business models to find ways to pay for these innovations. The main drivers behind this radical change in media production are consumers' changing media habits, cheaper digital technology, and the disruptive forces that these two drivers generate. Technology also makes possible new forms of storytelling, which potentially allows journalists the chance to do better journalism through convergence. This article focuses on the key issue of whether editorial managers and journalists are embracing convergence to save money, or to do better journalism. It begins by defining convergence (while accepting the wide variety of definitions) and describing two main models of implementation. It then considers the factors that lead to easy introduction of convergence followed by the factors that hinder its introduction. Examples are provided of converged media around the world. This article ends with a warning about the dangers for democracy of misapplied convergence in an era of increasing concentration of ownership.

Deterministic versus stochastic quantitative microbial Risk Assessment models for wastewater irrigation of food crops

Reuse of wastewater to irrigate food crops is being practiced in many parts of the world and is becoming more commonplace as the competition for, and stresses on, freshwater resources intensify. But there are risks associated with wastewater irrigation, including the possibility of transmission of pathogens causing infectious disease, to both workers in the field and to consumers buying and eating produce irrigated with wastewater. To manage these risks appropriately we need objective and quantitative estimates of them. This is typically achieved through one of two modelling approaches: deterministic or stochastic. Each parameter in a deterministic model is represented by a single value, whereas in stochastic models probability functions are used. Stochastic models are theoretically superior because they account for variability and uncertainty, but they are computationally demanding and not readily accessible to water resource and public health managers. We constructed models to estimate risk of enteric virus infection arising from the consumption of wastewater-irrigated horticultural crops (broccoli, cucumber and lettuce), and compared the resultant levels of risk between the deterministic and stochastic approaches. Several scenarios were tested for each crop, accounting for different concentrations of enteric viruses and different lengths of environmental exposure (i.e. the time between the last irrigation event and harvest, when the viruses are liable to decay or inactivation). In most situations modelled the two approaches yielded similar estimates of risk (within 1 order-of-magnitude). The two methods diverged most markedly, up to around 2 orders-of-magnitude, when there was large uncertainty associated with the estimate of virus concentration and the exposure period was short (1 day). Therefore, in some circumstances deterministic modelling may offer water resource managers a pragmatic alternative to stochastic modelling, but its usefulness as a surrogate will depend upon the level of uncertainty in the model parameters.

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.

Accuracy and precision assesment of stochastic simulation tools for springback variation

The sheet forming industry is plagued by inherent variations in its many input variables, making quality control and improvements a major hurdle. This is particularly poignant for Advanced High Strength Steels (AHSS), which exhibit a large degree of property variability. Current FE-based simulation packages are successful at predicting the manufacturability of a particular sheet metal components, however, due to their numerical deterministic nature are inherently unable to predict the performance of a real-life production process. Though they are now beginning to incorporate the stochastic nature of production in their codes. This work investigates the accuracy and precision of a current stochastic simulation package, AutoForm Sigma v4.1, by developing an experimental data set where all main sources of variation are captured through precise measurements and standard tensile tests. Using a Dual Phase 600Mpa grade steel a series of semi-cylindrical channels are formed at two Blank Holder Pressure levels where the response metric is the variation in springback determined by the flange angle. The process is replicated in AutoForm Sigma and an assessment of accuracy and precision of the predictions are performed. Results indicate a very good correspondence to the experimental trials, with mean springback response predicted to within 1 ° of the flange angle and the interquartile spread of results to within 0.22°.

Stability and convergence analysis of transform-domain LMS adaptive filters with second-order autoregressive process

In this paper, the stability and convergence properties of the class of transform-domain least mean square (LMS) adaptive filters with second-order autoregressive (AR) process are investigated. It is well known that this class of adaptive filters improve convergence property of the standard LMS adaptive filters by applying the fixed data-independent orthogonal transforms and power normalization. However, the convergence performance of this class of adaptive filters can be quite different for various input processes, and it has not been fully explored. In this paper, we first discuss the mean-square stability and steady-state performance of this class of adaptive filters. We then analyze the effects of the transforms and power normalization performed in the various adaptive filters for both first-order and second-order AR processes. We derive the input asymptotic eigenvalue distributions and make comparisons on their convergence performance. Finally, computer simulations on AR process as well as moving-average (MA) process and autoregressive-moving-average (ARMA) process are demonstrated for the support of the analytical results.

Reconstruction of in vivo time-evolving neuroendocrine dose - response properties unveils admixed deterministic and stochastic elements

Homeostasis in the intact organism is achieved implicitly by repeated incremental feedback (inhibitory) and feedforward (stimulatory) adjustments enforced via intermittent signal exchange. In separated systems, neurohormone signals act deterministically on target cells via quantifiable effector-response functions. On the other hand, in vivo interglandular signaling dynamics have not been estimable to date. Indeed, experimentally isolating components of an interactive network definitionally disrupts time-sensitive linkages. We implement and validate analytical reconstruction of endogenous effector-response properties via a composite model comprising (i) a deterministic basic feedback and feedforward ensemble structure; (ii) judicious statistical allowance for possible stochastic variability in individual biologically interpretable dose–response properties; and (iii) the sole data requirement of serially observed concentrations of a paired signal (input) and response (output). Application of this analytical strategy to a prototypical neuroendocrine axis in the conscious uninjected horse, sheep, and human (i) illustrates probabilistic estimation of endogenous effector dose–response properties; and (ii) unmasks statistically vivid (2- to 5-fold) random fluctuations in inferred target-gland responsivity within any given pulse train. In conclusion, balanced mathematical formalism allows one to (i) reconstruct deterministic properties of interglandular signaling in the intact mammal and (ii) quantify apparent signal-response variability over short time scales in vivo. The present proof-of-principle experiments introduce a previously undescribed means to estimate time-evolving signal-response relationships without isotope infusion or pathway disruption.

Narrativization of actuality : convergence of form and genre in film and television

The thesis concerns the treatment of actuality in film and television, particularly the narrativization of actuality images, and the context of their placement within audio/visual texts. Several instances of the convergence of media form and genre are analyzed, and the conventions of classificatory systems and boundaries that pertain to film and television representations are reconsidered in light of changes in the conventions of genre. The distinction between, and convergence of fictional and non-fictional conventions of narrative are therefore central to the thesis, as are the related issues of viewer response, the nature of subjectivity in the viewer, the connectivity of text and culture, and the relations of actuality to the text. The thesis traces the narrativization of actuality through textual, formal and genre boundaries, adopting a ‘line of flight or deterritorialization’ that enables the thesis to ‘change in nature and connect with other multiplicities.’This line of flight passes through the conventional separation of genre groupings and texts, and, similarly, has been applied in the thesis as a rationale for the diminution of theoretical boundaries. A multiperpectival approach is applied to the permeability of, or transcendent relations of the analysis to the boundaries between genres, between texts and culture, and between actuality and virtual representation. In the thesis there is also a theoretical deterritorialization that consents to a pluralism of theory, which is an approach demonstrated by Deleuze and Guattari in A Thousand Plateaus. The model of multi-perspectivalism adopted in the thesis engages in establishing connections and similarities between theories, rather than emphasizing contradictory and exclusive practices. The Foucauldian notion of the rules of formation in discourse, Nichols’ theories of documentary representation of reality, Bordwell’s schematic interpretation, and several other positions are critiqued, as the line of flight embarked upon in the thesis intersects with, and passes through both textual and theoretical boundaries. The thesis consists of two parts: firstly, a location of theoretical perspective, in which the issues of theory pertaining to actuality and narrative are explicated, and the methodological approach of the thesis is defined. The second part commences with an analysis of the most familiar instances of actuality in film and television, with particular attention to documentary forms. It then engages in the analysis of films that represent actuality but which, in the process of narrativization, display a convergence of genre conventions. The films selected for analysis include Steven Speilberg's Schindler's List, (1993) Oliver Stone's JFK, (1991) and Robert Zemeckis' Forrest Gump, (1994) and Contact, (1996). Hence the thesis is concerned with the application of a pluralist theoretical approach, with, however, an emphasis on the Deleuzo-Guattarian notions of rhizome and assemblage. Within this theoretical frame, the connections between actuality and the audio/visual text are explicated, and the formation of text as ‘a rhizome with the world’, is analyzed across a range of examples.

Accounting convergence of intangibles : value relevance of R & D accounting treatment

The rapid globalization of markets has resulted in further acceleration of the worldwide convergence of accounting standards, including the development of high-quality and globally consistent accounting standards for both domestic and cross-border financial reporting. Prior studies report that earnings value relevance of U.S. companies is decreasing, and the decline is partially attributable to the immediate expensing of intangibles, like research and development (R&D). Being one of the areas of divergence, accounting for R&D was highlighted as one of the seven short-term convergence projects by the FASB and IASB. Australia's adoption of IFRS in 2005 presents an empirical setting to evaluate the value relevance of different accounting treatments for R&D, and ultimately assist financial accounting standard setters in the U.S. and abroad in finding an appropriate accounting treatment for R&D.

A stochastic derivation of the ratio form of contest success functions

This note provides a distribution-based justification for the ratio form of contest success functions (CSFs), in which a player’s success depends positively on her effort relative to that of her opponents. I show that the inverse exponential distribution of the random shocks yields the ratio form. Extending this approach to asymmetric contests, I also derive an asymmetric ratio form of CSFs.