Science of science — using AI tools to effectively learn new scientific fields 4010-SOS-30
Module 1 — Science as an institution: norms, credit, and boundaries
What this module covers: Science as a distinctive institution with norms, priority rules, and reward systems; how boundaries between “science” and “non-science” are drawn; what counts as legitimate expertise.
Key questions: What norms sustain trust? How do credit and prestige shape behaviour? How do demarcation disputes work?
Module 2 — Foundations in SSK: explaining scientific knowledge socially
What this module covers: Sociology of Scientific Knowledge (SSK), symmetry, and the shift from studying institutions to studying the content and practices of knowledge-making; classic points of dispute.
Key questions: What does SSK explain well? Where are its limits? How do we connect practices, institutions, and epistemic claims?
Module 3 — Research strategies: exploration vs. exploitation; novelty and change
What this module covers: Strategic choices in research (risk, novelty, tradition), and how fields evolve; optional philosophical framing of scientific change (Kuhn/Lakatos).
Key questions: When does the system reward safe work over risky work? How do we operationalise novelty? What slows or accelerates progress?
Module 4 — Knowledge flows I: places, mobility, tacit knowledge
What this module covers: Why geography and co-location still matter; tacit knowledge; transmission constraints and the conditions of successful knowledge transfer.
Key questions: Why can’t knowledge always be “written down”? What role do networks and movement play?
Module 5 — Knowledge flows II: texts, citations, status, and platforms
What this module covers: How texts carry and shape knowledge; citation practices; status effects; platforms and infrastructures (e.g., Wikipedia) influencing science.
Key questions: What do citations measure (and what do they miss)? How does status change reading/citing behaviour?
Module 6 — Collaboration and team science
What this module covers: Growth of teams, team size and innovation, collaboration costs, and how collaboration structures affect “disruptive” vs “developmental” science.
Key questions: When do teams boost innovation? When do they reduce originality? What collaboration structures work best?
Module 7 — The economics and evaluation of science: funding, peer review, and trust in the literature; “future science”
What this module covers: Funding incentives and research direction; peer review and evaluation; bias and publication incentives; reproducibility; and emerging pressures (data deluge, AI opacity).
Key questions: Do evaluation systems select the best ideas? What drives unreliable findings? How do Big Data and AI change epistemic practice?
Prerequisites (description)
Course coordinators
Type of course
Mode
Learning outcomes
The student knows and understands:
W1 - [K_W02] - advanced methods of designing and analyzing the computational complexity of algorithms used in SoS
W2 - [K_W03] - operating principles and applications of the most important algorithms used in computer simulations utilized in SoS
W3 - [K_W04] - methods of statistical analysis of data used in SoS
W4 - [K_W05] - at least one programming language used in SoS
W5 - [K_W07] - current development trends and the latest discoveries in the field of network technologies and computer architectures used in SoS
W6 - [K_W08] - applications of numerical calculations used in SoS
The student is able to:
U1 - [K_U07] - determine directions for further learning and implement the self-education process
U2 - [K_U09] - use modern information and communication techniques to communicate with others
U3 - [K_U10] - plan and conduct computer simulations, analyze their results, and draw conclusions
U4 - [K_U11] - perform numerical calculations typical for SoS
U5 - [K_U13] - assess the suitability and possibility of using new hardware and software solutions to solve computational problems in SoS
U6 - [K_U14] - adhere to occupational health and safety principles in the profession of a computer scientist/data analyst in SoS
U7 - [K_U15] - conduct a preliminary economic analysis regarding hardware and software solutions needed for SoS
U8 - [K_U17] - analyze a problem and determine algorithms and computational methods useful for its solution
U9 - [K_U18] - design and analyze distributed algorithms; justify their correctness and analyze complexity in the context of SoS
U10 - [K_U20] - design efficient algorithms and justify their correctness for solutions applied in SoS
The student is ready to:
K1 - [K_K02] - establish and maintain cooperation with others; strives to achieve team goals through appropriate planning and organization of their own work and that of others
K2 - [K_K03] - creative thinking in order to identify problems, improve existing solutions, or create new ones
K3 - [K_K04] - independent and effective work with large amounts of data; perceives dependencies and correctly draws conclusions using the principles of logic
K4 - [K_K05] - execute the task as well as possible; takes care of details; is systematic
K5 - [K_K07] - continuously acquire new knowledge, skills, and experiences; has the desire for continuous self-improvement and raising professional competencies
K6 - [K_K10] - observe principles and norms applicable in the computer scientist profession, including ethical norms; understands the social role of the computer scientist profession
Assessment criteria
The student receives credit for the course based on:
- active participation in class,
- submitting module reports,
- exam,
- attendance.
NOTE
1. A note from a doctor does not exempt the student from submitting the report.
2. Attendance is mandatory. In justified cases of absence, the student is required to contact the course coordinator without delay.
Practical placement
Not applicable.
Bibliography
Selected literature:
Merton, Robert K. “The Normative Structure of Science.”
Polanyi, Michael. “The Republic of Science: Its Political and Economic Theory.”
Shapin, Steven. “Here and everywhere: Sociology of scientific knowledge.”
Latour, Bruno. “Give me a laboratory and I will raise the world.”
Uzzi, Brian, et al. “Atypical combinations and scientific impact.”
Science of Science
Kuhn, Thomas. Selections on anomaly/crisis (in The Structure of Scientific Revolutions).
Collins, Harry M. “The TEA Set: Tacit Knowledge and Scientific Networks.”
Teplitskiy, Misha, et al. “How status of research papers affects the way they are read and cited.”
Zuckerman, Harriet & Merton, Robert K. “Institutionalized Patterns of Evaluation in Science.”
Anderson (WIRED). “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.”
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: