Über Georges Canguilhems Was heißt eine wissenschaftliche Ideologie? und über deutsch‐französische Beiträge zum Thema Wissenschaft und Ideologie aus den letzten vierzig Jahren

On Georges Canguilhem's What does a Scientific Ideology mean? and on French‐German Contributions on Science and Ideology in the Last Fourty Years. This paper is based on Canguilhem's text on the concept of scientific ideology, which he introduced in 1969. We describe Canguilhem's attempts at designing a methodological framework for the history of science including the status of kinds of knowledge related to science, like scientific ideologies preceding particular scientific domains (like ideologies about inheritance before Mendel, or Spencer's universal evolutionary laws preceding Darwin). This attempt at picturing the relationships between science and ideology is compared with Jürgen Habermas's book Technology and Science as ‘Ideology’ in 1968. The philosphical issue of human normativity provides the framework of this discussion.     

STRUCTURES SOCIO-DEMOGRAPHIQUES ET REVOLUTION INDUSTRIELLE: Verviers, 1806–1846

Abstract

The author constructed measures of market structure for the music recording industry for the years 1890 to 1988. This measure can be used both to test a variety of hypotheses about the industry itself and to serve as part of a broader study of culture-based industries. For example, data were used to test a hypothesis relating market structure to the diversity of products issued by the industry (P. J. Alexander 1997). The main finding was that a moderately concentrated market structure provided greater product diversity than an atomistic or monopolistic market structure. Whereas census data and previous studies of the industry provide some information about industry structure, these sources are severely limited in scope and detail. Finally, similar rankings in other culture-based industries (e.g., broadcast television, radio, cable television) might provide a useful index of concentration in those industries.     

ART‐P‐MAP Neural Networks Modeling of Land‐Use Change: Accounting for Spatial Heterogeneity and Uncertainty

Spatial land‐use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART‐Probability‐Map (ART‐P‐MAP), tailored to address these issues in the context of spatial modeling of land‐use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell‐level uncertainty measurement. Compared with two baselines, ART‐P‐MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land‐use change problem. Finally, we conclude that, as a general probabilistic regression model, ART‐P‐MAP is applicable to a broad range of land‐use change modeling approaches, which deserves future research.