EMFE3100 Automasjon i bygg - ingeniørfaglig systememne Emneplan

The street-level bureaucracy perspective addresses the present question of what happens when politics meets reality. In different welfare state context as well as different policy areas answers to this question are important to search for. Street-level organizations (SLOs) form the operational centre of the welfare state, operating at the interface between policies on the page and policies in practice. The study of SLOs is at the cutting edge of new policy and management research that is investigating the development and implementation of social and labour market policy. The field builds on Lipsky's seminal work (1980) on street-level bureaucracy, which has influenced diverse types of research on public policy, management, sociology, and politics. Research in this field investigates the policy-making role of front-line organizations and those who work in them. It recognizes that front-line organizations play an active and crucial role in determining about 'who gets what, when and how' (Lasswell 1936). In this sense, the behaviour of street-level organizations might be seen as the 'continuation of politics by other means.' The course draws on on-going research and international collaboration on questions such as: what kinds of developments of governance and managerial strategies seek to influence the work of street-level organizations and how do these strategies influence policy delivery? How do labour market and social policies target unemployed and marginalised populations and the organizational practices through which they are realized?

The purpose of this PhD course is to use the street-level perspective to challenge current PhD projects and to discuss the question of policies on the page as opposed to policies in practice relevant for these projects. The course introduces the PhD students to different approaches and discussions within street-level bureaucracy research within the social sciences.

The course is essentially theoretical, aiming to introduce the students to different street-level perspectives as well as qualifying them to use these perspectives to address questions of politics and practice relevant for the individual PhD project.

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Target Group

The course is aimed at PhD students.

Emnet bygger på emner fra 1. og 2. studieår.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

Upon successful completion of this course, the student knows:

• theoretical and practical aspects of data management and data processing for different data types (electrophysiology, imaging, and behavior), so they can independently engage with the state-of-the art literature.
• how statistics, dimensionality reduction, signal processing, calculus, and supervised and unsupervised learning may be applied to data analysis in neuroscience research.
• commonly encountered problems in data science and the most trusted strategies to solve them.
• basic data visualization techniques and how to employ them in exploratory data analysis and scientific communication.

Skills

Upon successful completion of this course, the student can:

• apply data analysis techniques to formulate a hypothesis, collect data, preprocess it, analyze it, and reach conclusions about the data.
• plan, design and implement data analysis pipelines for the most common types of data in neuroscience.
• identify and define a problem and craft a solution using data analytics.
• critically assess the results as well as justify and explain the methodological choice.
• identify new opportunities for organizational change including process improvements, cost reduction, or efficiency improvements.

General competence

Upon successful completion of this course the student can apply:

• data analysis principles to neuroscientific data in the context of its own research.
• methods and tools for data analysis and visualization.
• heuristics and strategies commonly used in the research field to solve data analysis problems.

The teaching methodology is oriented by Bloom's taxonomy of educational goals, namely, recollection, understanding, application, analysis, evaluation, and creativity. To promote recollection, understanding, and application, the course will consist of seminars taught by the teaching staff of OsloMet and other guests (experts in neuroscience or data science fields), coding workshops and problem solving oriented projects. Students will actively participate by implementing the full data processing pipeline from extracting the raw data to building visualizations. The pipeline and good habits will be consolidated through repetition in different modules, contexts, and data types, which is known to promote generalization of the knowledge. Organized in pairs, the students will constantly have the opportunity to recollect, explain the content to each other, and justify their work, as well as to provide feedback to their partners. With the intent to prepare the students to go beyond the methods taught in the course, once per module, the participants will read relevant papers in neuroscience, and discuss how to implement their analysis.

The language of instruction is English, and all discussions, presentations etc. will be in English. The course will combine lectures on general theoretical and methodological issues from street- level bureaucracy with lectures reviewing and discussing substantive research and literature. However, a substantial part of the course will be dedicated to address the research projects of the students using the street-level perspective to push the research ideas, methodological considerations and analyses further. Student presentations are pivotal in discussing analytical options and theoretical inspirations for the development of the students own research.

The number of students is limited to max. 20. Each student is expected to play an active part in the course (please refer to section "Work Requirements"). The course literature is expected to be read before the course.

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Practical training

The students will gain practical training in academic discussions on others and own work related to street-level bureaucracy research. This include presentations and active involvement as discussants.

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The assessment will be based on the following:

• A paper written in English, to be presented and discussed during the course, has to be submitted by a set date given by Centre for the Study of Professions ;
• Students shall take part in discussions during the course, prepare and give comments to fellow students' papers

The paper is assessed by the course responsible and visiting academic contributors to the course. The paper is assessed on the basis of the stated learning outcomes for the course.

The paper should take a point of departure in the research of the student, but needs to address questions of policy into practice or include one or more of the elements from the street-level bureaucracy literature. The paper shall be between 8 and 15 pages (approx. 3200-6000 words), one and a half spaced, with ample margins.

Grading Scale

Pass or Fail

Support materials for assessment/examination

All

The course deals with several important issues in street-level research. First, the course deals with the history and development of the street-level bureaucracy tradition. Secondly, the course investigates several examples of empirical research within this tradition introducing the students to the vast empirical ground that street-level research covers.

Content:

• The black hole of Democracy
• Coping strategies across time and Space
• The street-level as a resource for positive feedback, reflection and reform of the welfare state
• The street-level as individual, organisational or homogenous across Fields
• Translating policy on paper to policy in practice
• Analytical approaches to policy analysis that are open to the street-level perspective;;

Gradert skala A-F.

En intern sensor. Ekstern sensor brukes jevnlig.