Kognitiva system i namngivningen av finländska handelsfartyg 1838-1938

This dissertation is an onomastic study of Finland s stock of ship names (nautonomasticon) recorded over the period 1838 1938. The primary material investigated consists of 2 066 examples of ship names from the fleets of coastal towns, distributed over five sample years. The material is supplemented with two bodies of comparative data; one that consists of 2 535 examples of boat names from the archipelago area at the corresponding time, and another that comprises 482 examples of eighteenth century Finnish ship names. This study clarifies the categories of names that appear the frequency of the names, formation, morphology, linguistic origin, functions, and semantic qualities. By comparing the material with boat names from previous centuries, and from other countries, the characteristics of Finnish vessel names are further highlighted. Additional clarification is brought to the chronological, regional, and social variations, and to the emergence of various forms of systematic naming. This dissertation builds on older research from other countries, and uses traditional onomastic methods alongside a more modern methodology. The approach is interdisciplinary, meaning that the names are explored using facts not only from nautical history, but also from a range of other historical disciplines such as economics, culture, art, and literature. In addition, the approach is socio-onomastic, i.e. that the variations in names are studied in a societal context. Using a synchronised perspective, cognitive linguistic theories have provided the tools for this exploration into the metaphorical and the prototypical meaning of the names, and the semantic domains that the names create. The quantitative analysis has revealed the overall picture of Finnish boat names. Personal names, names from mythology, and place names, emerge as significant categories, alongside nonproprial names in Swedish and Finnish. The interdisciplinary perspective has made it possible to explain certain trends in the stock of boat names, for example, the predisposition towards names from classical mythology, the breakthrough of names taken from the national epos Kalevala, names in the Finnish language from around the middle of the nineteenth century, and the continuing rise of place names during the latter part of the period 1838 1938. The socio-onomastic perspective has also identified clear differences between those ship names used in towns, and those ship names used in the archipelago, and it has clarified how naming conventions tend to spread from town centres to peripheral areas. The cognitive linguistic methods have revealed that the greater part of the vessel names can be interpreted as metaphors, in particular personifications, and that many names are related in their content and also form semantic networks and cognitive systems. The results indicate that there is a mental nautonomasticon that consists of a standard set of traditional ship names, but they also reveal the existence of conscious or unconscious cognitive systems (rules and conventions) that guide the naming of boats.

Bayesian fisheries stock assessment: integrating and updating knowledge

In this thesis the use of the Bayesian approach to statistical inference in fisheries stock assessment is studied. The work was conducted in collaboration of the Finnish Game and Fisheries Research Institute by using the problem of monitoring and prediction of the juvenile salmon population in the River Tornionjoki as an example application. The River Tornionjoki is the largest salmon river flowing into the Baltic Sea. This thesis tackles the issues of model formulation and model checking as well as computational problems related to Bayesian modelling in the context of fisheries stock assessment. Each article of the thesis provides a novel method either for extracting information from data obtained via a particular type of sampling system or for integrating the information about the fish stock from multiple sources in terms of a population dynamics model. Mark-recapture and removal sampling schemes and a random catch sampling method are covered for the estimation of the population size. In addition, a method for estimating the stock composition of a salmon catch based on DNA samples is also presented. For most of the articles, Markov chain Monte Carlo (MCMC) simulation has been used as a tool to approximate the posterior distribution. Problems arising from the sampling method are also briefly discussed and potential solutions for these problems are proposed. Special emphasis in the discussion is given to the philosophical foundation of the Bayesian approach in the context of fisheries stock assessment. It is argued that the role of subjective prior knowledge needed in practically all parts of a Bayesian model should be recognized and consequently fully utilised in the process of model formulation.