HPSC3108: Semester 1, 2021

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Units HPSC3108 Semester 1 2021 [Normal day]

Unit outline_

HPSC3108: Hist and Phil of the Physical Sciences

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit of study deals with a selection of contemporary debates in the history and philosophy of natural sciences. It covers four main themes: (1) the question of how evidence is gathered in the natural sciences and how it is (and/or other factors) go into confirming theories-we also consider what confirmation consists in (including an examination of Bayesianism). (2) Issues of modelling, representation, and measurement, including an analysis of the ways idealisation, approximation, and simulation are to be understood. (3) Models of scientific explanation, including recent work on laws, prediction, and causality. (4) issues of emergence and reduction, including the problems associated with defining such concepts - we also consider notions of simplicity and the impact of the sciences of complexity. The unit of study involves case studies from the natural sciences that allow students to apply their knowledge and test their understanding. Upon completion of the unit, students will have developed a range of skills that will allow them to explore the physical sciences with more critical attitude.

Unit details and rules

Academic unit	History and Philosophy of Science Academic Operations
Credit points	6
Prerequisites ?	HPSC1X01 or HPSC2X01
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Peter Godfrey-Smith, peter.godfrey-smith@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Essay 2 Written essay	40%	STUVAC Due date: 04 Jun 2021 at 22:00	2500 words
Outcomes assessed:
Assignment	Exercise 1 Short written exercise	2.5%	Week 04 Due date: 25 Mar 2021 at 23:00	Approx 200 words
Outcomes assessed:
Assignment	Exercise 2 Short written exercise	2.5%	Week 05 Due date: 01 Apr 2021 at 23:00	Approx 200 words
Outcomes assessed:
Assignment	Exercise 3 Written exercise	2.5%	Week 06 Due date: 15 Apr 2021 at 23:00	Approx 200 words
Outcomes assessed:
Assignment	Essay 1 Written essay	40%	Week 07 Due date: 22 Apr 2021 at 23:00	2500 words
Outcomes assessed:
Assignment	Exercise 4 Short exercise	2.5%	Week 08 Due date: 29 Apr 2021 at 23:00	Approx 200 words
Outcomes assessed:
Participation	Participation Participation	10%	Week 12	Throughout
Outcomes assessed:

Assessment summary

Writing task (essay): This assignment will require you to integrate information from lectures and readings to create a concise written argument.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Overview of the course and its themes	Lecture and tutorial (2 hr)
Week 02	2. Some different views of evidence	Lecture and tutorial (2 hr)
Week 03	3. The Bayesian model, part 1	Lecture and tutorial (2 hr)
Week 04	4. The Bayesian model, part 2	Lecture and tutorial (2 hr)
Week 05	5. Beliefs, values, and policy choices	Lecture and tutorial (2 hr)
Week 06	6. Bayesianism and social structure	Lecture and tutorial (2 hr)
Week 07	7. Evidence and truth	Lecture and tutorial (2 hr)
Week 08	8. Models and simulations	Lecture and tutorial (2 hr)
Week 09	9. Models and fictions	Lecture and tutorial (2 hr)
Week 10	10. Models, fictions, and conditionals	Lecture and tutorial (2 hr)
Week 11	11. Putting some pieces together (evidence, belief, truth, models, conditionals, policies)	Lecture and tutorial (2 hr)
Week 12	12. Wrap-up.	Lecture and tutorial (2 hr)

Attendance and class requirements

Attendance policies will be discussed when we reach the start of term.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Readings will be made available on the Canvas website.

You should read the material for that week before the Monday lecture.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Understand a leading model of how evidence works in science (the Bayesian model) and how it relates to a variety of other problems and topics.
LO2. understand current views on the nature of scientific models, and how they represent the world
LO3. demonstrate an understanding of some of the most pressing issues in contemporary philosophy of science.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

N/A

Additional information

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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