Validation of Soft Classification Models using Partial Class Memberships: An Extended Concept of Sensitivity & Co. applied to the Grading of Astrocytoma Tissues

We use partial class memberships in soft classification to model uncertain labelling and mixtures of classes. Partial class memberships are not restricted to predictions, but may also occur in reference labels (ground truth, gold standard diagnosis) for training and validation data. Classifier performance is usually expressed as fractions of the confusion matrix, such as sensitivity, specificity, negative and positive predictive values. We extend this concept to soft classification and discuss the bias and variance properties of the extended performance measures. Ambiguity in reference labels translates to differences between best-case, expected and worst-case performance. We show a second set of measures comparing expected and ideal performance which is closely related to regression performance, namely the root mean squared error RMSE and the mean absolute error MAE. All calculations apply to classical crisp classification as well as to soft classification (partial class memberships and/or one-class classifiers). The proposed performance measures allow to test classifiers with actual borderline cases. In addition, hardening of e.g. posterior probabilities into class labels is not necessary, avoiding the corresponding information loss and increase in variance. We implement the proposed performance measures in the R package "softclassval", which is available from CRAN and at http://softclassval.r-forge.r-project.org. Our reasoning as well as the importance of partial memberships for chemometric classification is illustrated by a real-word application: astrocytoma brain tumor tissue grading (80 patients, 37000 spectra) for finding surgical excision borders. As borderline cases are the actual target of the analytical technique, samples which are diagnosed to be borderline cases must be included in the validation.Comment: The manuscript is accepted for publication in Chemometrics and Intelligent Laboratory Systems. Supplementary figures and tables are at the end of the pd

Identifying Contextual Factors of Employee Satisfaction of Performance Management at a Thai State Enterprise

Although there has been an increase in Performance Management (PM) literature over the years arguing that PM perceptions are likely to be a function of PM process components and contextual factors, the actual relationship between the contextual factors and employee satisfaction of PM remains little explored.  Extending previous research, this study examines relationships between contextual factors and employees’ PM satisfaction.  Derived from the literature, these contextual factors are motivation and empowerment of employees, role conflict, role ambiguity, perceived organisational support, procedural justice and distributive justice.  Seven directional hypotheses are tested accordingly through a series of regression analyses.  This article finds that these contextual factors, with the exception of role conflict, are directly predictive of enhanced employees’ PM satisfaction at the Thai state enterprise

Risk frames and multiple ways of knowing : Coping with ambiguity in oil spill risk governance in the Norwegian Barents Sea

The opening of new areas for offshore drilling in the Arctic is highly controversial. As ice cover in the region is melting at an alarming rate, new areas have been opened for petroleum industry in the Norwegian Barents Sea. Our qualitative analysis examines risks related to the petroleum operations in the newly opened areas and provides insight into the complex and socially constructed nature of the risks. With the use of visual influence diagram- based mental modelling approach, we demonstrate the multiple ways in which the risks are understood and defined. We also analyse the type of knowledge that the risk frames are based on. The influence diagrams present the risk frames in a clear, visual, form. The study indicates that the existing governance framework fails to treat the ambiguity around oil spill risks: the current risk assessments and risk management do not reflect on the multiple ways in which the participants in this study 1) frame the problem situation, 2) how they identify different measures to manage risks, and 3) what are considered as key knowledge needs and knowledge producers by the participants. We suggest that social learning and collaborative knowledge production are needed to move towards developing shared understanding of the problem situation. Finally, we suggest that the rigorous examination and the unveiling of ambiguity may help developing deliberative risk governance measures and moving towards sustainability transformations.Peer reviewe

Toward a relational concept of uncertainty: about knowing too little, knowing too differently, and accepting not to know

Uncertainty of late has become an increasingly important and controversial topic in water resource management, and natural resources management in general. Diverse managing goals, changing environmental conditions, conflicting interests, and lack of predictability are some of the characteristics that decision makers have to face. This has resulted in the application and development of strategies such as adaptive management, which proposes flexibility and capability to adapt to unknown conditions as a way of dealing with uncertainties. However, this shift in ideas about managing has not always been accompanied by a general shift in the way uncertainties are understood and handled. To improve this situation, we believe it is necessary to recontextualize uncertainty in a broader way¿relative to its role, meaning, and relationship with participants in decision making¿because it is from this understanding that problems and solutions emerge. Under this view, solutions do not exclusively consist of eliminating or reducing uncertainty, but of reframing the problems as such so that they convey a different meaning. To this end, we propose a relational approach to uncertainty analysis. Here, we elaborate on this new conceptualization of uncertainty, and indicate some implications of this view for strategies for dealing with uncertainty in water management. We present an example as an illustration of these concepts. Key words: adaptive management; ambiguity; frames; framing; knowledge relationship; multiple knowledge frames; natural resource management; negotiation; participation; social learning; uncertainty; water managemen

Drafting 'better regulation": The economic cost of regulatory complexity

Different public agencies are seeking to draft ?better regulation?. Complex or poorly drafted norms are more difficult for economic agents to implement, tending to erode economic efficiency. The literature has so far concentrated on the analysis of regulatory complexity as a phenomenon related to the ?quantity? of norms. This article guides the process of adopting new regulations, taking into account that norms can also be complex due to new ?qualitative? reasons such as linguistic ambiguity or relational structure (references between legal documents). To perform the analysis, we develop new indicators for legibility and regulatory interconnectedness. Specifically, we construct a new database (RECOS ? REgulation COmplexity in Spain) by extracting information from 8171 norms (61 million words) which comprise the regulations of all the Spanish Autonomous regions. Our analysis reveals the relationship between measures of ?qualitative? complexity and relevant economic (productivity) and institutional (judicial efficacy) variables. This researc