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| 2. |
- Andersson, Thomas, et al.
(författare)
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When complexity meets culture : new public management and the Swedish police
- 2009
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Ingår i: Qualitative Research in Accounting & Management/Emerald. - : Emerald Group Publishing Limited. - 1176-6093 .- 1758-7654. ; 6:1/2, s. 41-56
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The purpose of this paper is to demonstrate how new public management (NPM) reform from the national level is implemented as practice in a local unit within the police sector in Sweden.Design/methodology/approach: A qualitative case-study approach is applied using semi-structured interviews, participant observations and analysis of documents.Findings: The paper illustrates different kinds of resistance at the organizational level. The dominant form of resistance was found to be cultural distancing. The paper demonstrates a tendency among police officers to deal with a changing and more complex work context by embracing a traditional work role.Research limitations/implications: The paper shows that reforms that add complexity may fail because of potential contradictions and the limited capacity and motivation of employees to deal with the complexity in the manner prescribed by NPM. Practical implications: The paper shows that the popular trend to adopt multi-dimensional forms of control (for instance the balanced-scorecard approach) may fail if there is a lack of consensus about what goals and measurement are important and/or there is a lack of dialogue about how the new goals should be implemented in practice.Originality/value: Research about NPM-reforms in the police sector is rare. The original contribution of this paper is to study NPM-reforms with a focus on the role of complexity in relation to resistance.
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| 3. |
- Johannesson, Krister, 1970-
(författare)
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I främsta rummet : planerandet av en högskolebiblioteksbyggnad med studenters arbete i fokus
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The purpose of the thesis is to investigate planning processes for academic library buildings and the outcomes of such processes. This is accomplished through a case study utilising discourse analysis. The main question is: How is a vision of an academic library implemented in and through a building? The case study is retrospective and focused on the building of a new library at Kalmar University, Sweden, at the end of the 1990s. During this period, technological and educational developments and general societal change transformed the context of library planning and made way for renegotiations of the librarian profession. A critical realist approach characterises the study of visions, processes and the analysis of the various functions of the building. Results reveal the proactive nature of the activities of the library director in Kalmar. Early in the process he formulated a vision in which he presents the library as an information resource, a meeting place between different user groups and a workplace intended to promote learning and knowledge. From a professional point of view, the vision implied a dehierarchisation of relations both within the library staff and between library staff and visitors. The vision was based on an interpretation of Swedish national educational policy, and architecturally manifested by an ambition to reduce the physical and psychological boundaries between library staff and visitors. The early formulation of the vision together with the clients’ use of architectural expertise facilitated the choice of architects. However during the process a need arose to anchor the decision in the library field. Efforts were made to address library expertise and to collect user comments from a broader academic field. Discourses concerning the university library as a workplace and a meeting place were especially evident in the strategies of the leading agents. The discourses uncovered in the study correspond to more general discourses which became prominent in society and higher education during the period in question. The library itself has met growing appreciation by users both from within and outside the university. The proactive leadership demonstrated by the library director in Kalmar was based on hegemony rather than coercion. This corresponds to contemporary tendencies. Hegemonic consent may persist even after changes in leadership. In Kalmar however, architectural solutions with insufficient support from the library staff have been reconstructed after changes in leadership. Future research on architectural planning processes may pay further attention to different discursive resources, social fields and the positions within them.
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| 4. |
- Johansson Sundler, Annelie, et al.
(författare)
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The Meaning of Close Relationships and Sexuality : Women's Well-Being Following a Myocardial Infarction
- 2009
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Ingår i: Qualitative Health Research. - : Sage Publications. - 1049-7323 .- 1552-7557. ; 19:3, s. 375-387
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Tidskriftsartikel (refereegranskat)abstract
- Relationships and sexuality following heart attack (MI) have been studied; nevertheless, little is known about the meaning of social support and relationships to health and well-being after an MI. To our knowledge, no qualitative studies have further investigated the phenomenon. In this study we explore the meaning of close relationships and sexuality to women's health and well-being following MI. Ten women were interviewed using a reflective lifeworld approach and phenomenological epistemology. The meaning of women's close relationships following an MI appears to be closely intertwined with their long-term health process; both health processes and the relationships are affected. Suffering after an MI can be compared to taking a fall in that close relationships can become a safety net. Close relationships and sexuality are integrated into their lived bodies, and in that way have profound influence in their lifeworld experiences. Not all close relationships are intimate; however, all close and meaningful relationships can provide power and strength to the women's health processes. At the same time, these relationships also appear to drain energy and cause suffering.
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| 5. |
- Johansson, Ulf, et al.
(författare)
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Evolving decision trees using oracle guides
- 2009
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Ingår i: 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2009) Proceedings. - : IEEE. - 9781424427659 ; , s. 238-244
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Konferensbidrag (refereegranskat)abstract
- Abstract—Some data mining problems require predictive models to be not only accurate but also comprehensible. Comprehensibility enables human inspection and understanding of the model, making it possible to trace why individual predictions are made. Since most high-accuracy techniques produce opaque models, accuracy is, in practice, regularly sacrificed for comprehensibility. One frequently studied technique, often able to reduce this accuracy vs. comprehensibility tradeoff, is rule extraction, i.e., the activity where another, transparent, model is generated from the opaque. In this paper, it is argued that techniques producing transparent models, either directly from the dataset, or from an opaque model, could benefit from using an oracle guide. In the experiments, genetic programming is used to evolve decision trees, and a neural network ensemble is used as the oracle guide. More specifically, the datasets used by the genetic programming when evolving the decision trees, consist of several different combinations of the original training data and “oracle data”, i.e., training or test data instances, together with corresponding predictions from the oracle. In total, seven different ways of combining regular training data with oracle data were evaluated, and the results, obtained on 26 UCI datasets, clearly show that the use of an oracle guide improved the performance. As a matter of fact, trees evolved using training data only had the worst test set accuracy of all setups evaluated. Furthermore, statistical tests show that two setups, both using the oracle guide, produced significantly more accurate trees, compared to the setup using training data only.
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| 6. |
- König, Rikard
(författare)
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Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Today, decision support systems based on predictive modeling are becoming more common, since organizations often collect more data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. There are numerous predictive techniques, ranging from simple techniques such as linear regression, to complex powerful ones like artificial neural networks. Complex models usually obtain better predictive performance, but are opaque and thus cannot be used to explain predictions or discovered patterns. The design choice of which predictive technique to use becomes even harder since no technique outperforms all others over a large set of problems. It is even difficult to find the best parameter values for a specific technique, since these settings also are problem dependent. One way to simplify this vital decision is to combine several models, possibly created with different settings and techniques, into an ensemble. Ensembles are known to be more robust and powerful than individual models, and ensemble diversity can be used to estimate the uncertainty associated with each prediction.In real-world data mining projects, data is often imprecise, contain uncertainties or is missing important values, making it impossible to create models with sufficient performance for fully automated systems. In these cases, predictions need to be manually analyzed and adjusted. Here, opaque models like ensembles have a disadvantage, since the analysis requires understandable models. To overcome this deficiency of opaque models, researchers have developed rule extraction techniques that try to extract comprehensible rules from opaque models, while retaining sufficient accuracy.This thesis suggests a straightforward but comprehensive method for predictive modeling in situations with poor data quality. First, ensembles are used for the actual modeling, since they are powerful, robust and require few design choices. Next, ensemble uncertainty estimations pinpoint predictions that need special attention from a decision maker. Finally, rule extraction is performed to support the analysis of uncertain predictions. Using this method, ensembles can be used for predictive modeling, in spite of their opacity and sometimes insufficient global performance, while the involvement of a decision maker is minimized.The main contributions of this thesis are three novel techniques that enhance the performance of the purposed method. The first technique deals with ensemble uncertainty estimation and is based on a successful approach often used in weather forecasting. The other two are improvements of a rule extraction technique, resulting in increased comprehensibility and more accurate uncertainty estimations.
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| 7. |
- Löfström, Tuve, et al.
(författare)
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Ensemble member selection using multi-objective optimization
- 2009
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Ingår i: IEEE Symposium on Computational Intelligence and Data Mining. - : IEEE conference proceedings. - 9781424427659 ; , s. 245-251
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Konferensbidrag (refereegranskat)abstract
- Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. In essence, the key problem is to find a suitable criterion, typically based on training or selection set performance, highly correlated with ensemble accuracy on novel data. Several studies have, however, shown that it is difficult to come up with a single measure, such as ensemble or base classifier selection set accuracy, or some measure based on diversity, that is a good general predictor for ensemble test accuracy. This paper presents a novel technique that for each learning task searches for the most effective combination of given atomic measures, by means of a genetic algorithm. Ensembles built from either neural networks or random forests were empirically evaluated on 30 UCI datasets. The experimental results show that when using the generated combined optimization criteria to rank candidate ensembles, a higher test set accuracy for the top ranked ensemble was achieved, compared to using ensemble accuracy on selection data alone. Furthermore, when creating ensembles from a pool of neural networks, the use of the generated combined criteria was shown to generally outperform the use of estimated ensemble accuracy as the single optimization criterion.
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| 8. |
- Löfström, Tuve
(författare)
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Utilizing Diversity and Performance Measures for Ensemble Creation
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- An ensemble is a composite model, aggregating multiple base models into one predictive model. An ensemble prediction, consequently, is a function of all included base models. Both theory and a wealth of empirical studies have established that ensembles are generally more accurate than single predictive models. The main motivation for using ensembles is the fact that combining several models will eliminate uncorrelated base classifier errors. This reasoning, however, requires the base classifiers to commit their errors on different instances – clearly there is no point in combining identical models. Informally, the key term diversity means that the base classifiers commit their errors independently of each other. The problem addressed in this thesis is how to maximize ensemble performance by analyzing how diversity can be utilized when creating ensembles. A series of studies, addressing different facets of the question, is presented. The results show that ensemble accuracy and the diversity measure difficulty are the two individually best measures to use as optimization criterion when selecting ensemble members. However, the results further suggest that combinations of several measures are most often better as optimization criteria than single measures. A novel method to find a useful combination of measures is proposed in the end. Furthermore, the results show that it is very difficult to estimate predictive performance on unseen data based on results achieved with available data. Finally, it is also shown that implicit diversity achieved by varied ANN architecture or by using resampling of features is beneficial for ensemble performance.
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