Development and illustrative outputs of the Community Integrated Assessment System (CIAS), a multi-institutional modular integrated assessment approach for modelling climate change.

This paper describes the development and first results of the “Community Integrated Assessment System” (CIAS), a unique multi-institutional modular and flexible integrated assessment system for modelling climate change. Key to this development is the supporting software infrastructure, SoftIAM. Through it, CIAS is distributed between the community of institutions which has each contributed modules to the CIAS system. At the heart of SoftIAM is the Bespoke Framework Generator (BFG) which enables flexibility in the assembly and composition of individual modules from a pool to form coupled models within CIAS, and flexibility in their deployment onto the available software and hardware resources. Such flexibility greatly enhances modellers’ ability to re-configure the CIAS coupled models to answer different questions, thus tracking evolving policy needs. It also allows rigorous testing of the robustness of IA modelling results to the use of different component modules representing the same processes (for example, the economy). Such processes are often modelled in very different ways, using different paradigms, at the participating institutions. An illustrative application to the study of the relationship between the economy and the earth’s climate system is provided.

The European Declarative System, Database, and Languages

The EP2025 EDS project develops a highly parallel information server that supports established high-value interfaces. We describe the motivation for the project, the architecture of the system, and the design and application of its database and language subsystems. The Elipsys logic programming language, its advanced applications, EDS Lisp, and the Metal machine translation system are examined.

Giving up on modern foreign languages? Students' perceptions of learning French

This article reports on the findings of an investigation into the attitudes of English students aged 16 to 19 years towards French and how they view the reasons behind their level of achievement. Those students who attributed success to effort, high ability, and effective learning strategies had higher levels of achievement, and students intending to continue French after age 16 were more likely than noncontinuers to attribute success to these factors. Low ability and task difficulty were the main reasons cited for lack of achievement in French, whereas the possible role of learning strategies tended to be overlooked by students. It is argued that learners' self-concept and motivation might be enhanced through approaches that encourage learners to explore the causal links between the strategies they employ and their academic performance, thereby changing the attributions they make for success or failure.