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<CourseUnit xmlns="http://www.manchester.ac.uk/CUICourseUnitDetails" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.manchester.ac.uk/CUICourseUnitDetails.xsd">
  <UnitCode Applicant="Y" Label="Unit code" Student="Y">
    <Code>HIST10882</Code>
  </UnitCode>
  <UnitTitle Applicant="Y" Label="Unit title" Student="Y">
    <Title>Histories of Information</Title>
  </UnitTitle>
  <MaxUnits Applicant="Y" Label="Credit rating" Student="Y">
    <Units>20</Units>
  </MaxUnits>
  <TeachingPeriods Applicant="Y" Label="Teaching period(s)" Student="Y">
    <Period>Semester 2</Period>
  </TeachingPeriods>
  <AcademicCareer Applicant="Y" Label="Academic career" Student="Y">
    <Value>Undergraduate</Value>
  </AcademicCareer>
  <UnitLevel Applicant="Y" Label="Unit level" Student="Y">
    <Level>Level 4</Level>
  </UnitLevel>
  <StaffList Applicant="Y" Label="Teaching staff" RoleLabel="Course Unit Role" Student="Y">
    <StaffMember>
      <Name>Luca Scholz</Name>
      <Role>Unit coordinator</Role>
    </StaffMember>
  </StaffList>
  <OfferedBy Applicant="Y" Label="Offered by" Student="Y">
    <OrganisationList>
      <Organisation>
        <OrgName>History</OrgName>
      </Organisation>
    </OrganisationList>
    <GroupList>
      <Group>
        <GroupName></GroupName>
      </Group>
    </GroupList>
    <FheqLevels>
      <FheqLevel>
        <LevelNumber>1</LevelNumber>
        <LevelName>FHEQ level (Framework for Higher Education Qualifications) ' Undefined ' </LevelName>
      </FheqLevel>
    </FheqLevels>
    <Ects>
      <MaxUnits>European Credit Transfer &amp; Accumulation System Rating :   10.0</MaxUnits>
    </Ects>
  </OfferedBy>
  <MarketingOverview Applicant="Y" Label="Marketing Course unit overview" Student="">
    <Content>&lt;p&gt;Contemporary debates around artificial intelligence, big data, and the age of algorithms often carry a sense of novelty. While much is indeed new about how we interact with information today, information practices and technologies emerged globally over centuries. This course places contemporary enthusiasm and apprehension around data and its associated technologies – from diagrams to digital media – in a long historical perspective. Approaching data as an unstable category and as a historical source, and covering subjects ranging from eugenics to personal data, we will find that our present age is hardly the first in which human societies have agonized over the place of data in their lives. By examining forms and uses of information from the premodern era to the present we will develop a deeper understanding of how data and its cognate practices have been established and resisted in different historical and geographic settings.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </MarketingOverview>
  <UnitOverview Applicant="" Label="Course unit overview" Student="Y">
    <Content>&lt;p&gt;Contemporary debates around artificial intelligence, big data, and the age of algorithms often carry a sense of novelty. While much is indeed new about how we interact with information today, information practices and technologies emerged globally over centuries. This course places contemporary enthusiasm and apprehension around data and its associated technologies – from diagrams to digital media – in a long historical perspective. Approaching data as an unstable category and as a historical source, and covering subjects ranging from eugenics to personal data, we will find that our present age is hardly the first in which human societies have agonized over the place of data in their lives. By examining forms and uses of information from the premodern era to the present we will develop a deeper understanding of how data and its cognate practices have been established and resisted in different historical and geographic settings.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </UnitOverview>
  <Aims Applicant="Y" Label="Aims" Student="Y">
    <Content>&lt;p&gt;Place contemporary enthusiasm and apprehension around data and datafication in historical perspective.&lt;br&gt;Equip students with strong conceptual and methodological foundations for interacting with (digital) information and data and thinking about its history and biases.&amp;nbsp;&lt;br&gt;Support students in examining the relationships between current and historic information technologies and data practices in a variety of settings.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </Aims>
  <LearningOutcomes Applicant="Y" Label="Learning outcomes" Student="Y">
    <Content>&lt;p&gt;Upon completion of this course, students will be expected to have developed a range of transferable skills. Lectures, readings, and seminar discussions will allow students to develop their critical thinking skills and digital literacy and apply those to a wide range of examples. The first assignment will enable students to navigate a complex historical record, identify relevant material, and offer a succinct and precise discussion. The second assignment will enable students to conceive and pursue a small independent research project and enhance their work through peer feedback. Throughout, students will develop a situated, in-depth understanding of data, datafication, and their associated practices.&lt;/p&gt;</Content>
  </LearningOutcomes>
  <Knowledge Applicant="Y" Label="Knowledge and understanding" Student="Y">
    <Content>&lt;p&gt;Place contemporary debates and practices of data and datafication in a historical perspective.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Identity the relationships between current and historic data practices in a variety of contexts.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Demonstrate a robust academic vocabulary for interacting with and questioning data.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </Knowledge>
  <IntellectualSkills Applicant="Y" Label="Intellectual skills" Student="Y">
    <Content>&lt;p&gt;Situate and evaluate historical factors that have determined how data has been collected, organised, and made accessible or unavailable.&lt;/p&gt;&lt;p&gt;Examine and critique academic claims and arguments relating to the histories of data and datafication.&lt;/p&gt;&lt;p&gt;Identify, describe, and critically evaluate artifacts such as datasets, data visualisations, and related media.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </IntellectualSkills>
  <PracticalSkills Applicant="Y" Label="Practical skills" Student="Y">
    <Content>&lt;p&gt;Identify, retrieve, and analyse different types of historical data and secondary readings.&lt;/p&gt;&lt;p&gt;Compare and contrast a range of methods and tools used to interrogate and analyse data in historical inquiry.&lt;/p&gt;&lt;p&gt;Analyse and recast a complex type of evidence in terms that peers and non-experts can understand.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </PracticalSkills>
  <TransferableSkills Applicant="Y" Label="Transferable skills and personal qualities" Student="Y">
    <Content>&lt;p&gt;Think creatively about how to develop and communicate their work.&lt;/p&gt;&lt;p&gt;Reflect on and act upon peer and instructor feedback.&lt;/p&gt;&lt;p&gt;Summarise and present information and arguments with due regard to the target audience.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </TransferableSkills>
  <EmployabilitySkillsList Applicant="Y" Label="Employability skills" Student="Y">
    <Skill>
      <SkillId></SkillId>
      <SkillDescription></SkillDescription>
    </Skill>
  </EmployabilitySkillsList>
  <Syllabus Applicant="Y" Label="Syllabus" Student="Y">
    <Content></Content>
  </Syllabus>
  <TeachingMethods Applicant="Y" Label="Teaching and learning methods" Student="Y">
    <Content>&lt;p&gt;The unit consists of 2h-long lectures and 1h-long seminars a week, delivered in person.&lt;/p&gt;&lt;p&gt;Lectures will combine oral presentations with presentation slides, audio and video material, as well as interactive elements.&lt;/p&gt;&lt;p&gt;Seminars will give students an opportunity to critically discuss the readings and lectures and involve them in a series of practical and group activities, including but not limited to debates, tutorials, and peer review.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </TeachingMethods>
  <AssessmentMethods Applicant="Y" Label="Assessment methods" Student="Y">
    <IntroText> </IntroText>
    <Method>
      <MethodId>0</MethodId>
      <MethodName>Other</MethodName>
      <MethodWeight>100%</MethodWeight>
    </Method>
    <OtherDescription>&lt;p&gt;Peer review of essay ideas (Formative) 10 minutes&lt;/p&gt;&lt;p&gt;&lt;br&gt;Individual description of a historical instance of data (Summative) 30%&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;Individual essay about data in history or in contemporary life (Summative) 70%&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</OtherDescription>
  </AssessmentMethods>
  <FeedbackMethods Applicant="Y" Label="Feedback methods" Student="Y">
    <Content>&lt;p&gt;Via Turnitin within 15 working days&lt;/p&gt;</Content>
  </FeedbackMethods>
  <RequirementsList Applicant="Y" Label="Pre/co-requisites" Student="Y">
    <Requirement>
      <UnitCode></UnitCode>
      <UnitTitle></UnitTitle>
      <RequirementType></RequirementType>
      <Description></Description>
    </Requirement>
    <AdditionalRequirement></AdditionalRequirement>
  </RequirementsList>
  <AcademicPrograms Applicant="Y" Label="Academic programmes" Student="Y">
    <AcademicProgram>
      <Program></Program>
      <Plan></Plan>
      <Level></Level>
      <Requirement></Requirement>
    </AcademicProgram>
  </AcademicPrograms>
  <FreeChoice Applicant="Y" Label="Available as a free choice unit?" Student="Y">
    <Content>N</Content>
  </FreeChoice>
  <Accreditation Applicant="Y" Label="Accreditation" Student="Y">
    <Content></Content>
  </Accreditation>
  <RecommendedReading Applicant="Y" Label="Recommended reading" Student="Y">
    <Content>&lt;p&gt;Blair, Ann, Paul Duguid, Anja-Silvia Goeing, and Anthony Grafton (eds), Information: A Historical Companion. Princeton: Princeton University Press, 2021.&lt;/p&gt;&lt;p&gt;Bouk, Dan. “The History and Political Economy of Personal Data over the Last Two Centuries in Three Acts.” Osiris 32, no. 1 (2017): 85–106.&lt;/p&gt;&lt;p&gt;Crawford, Kate. “Archeologies of Datasets.” The American Historical Review 128, no. 3&lt;br&gt;(2023): 1368–71.&lt;/p&gt;&lt;p&gt;Edwards, Paul N. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, MA: MIT Press, 2010.&lt;/p&gt;&lt;p&gt;Halpern, Orit. Beautiful Data: A History of Vision and Reason since 1945. Durham, NC: Duke University Press, 2015.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Igo, Sarah E. “Me and My Data.” Historical Studies in the Natural Sciences 48, no. 5 (2018): 616–26.&lt;/p&gt;&lt;p&gt;Jones, Matthew L. “How We Became Instrumentalists (Again): Data Positivism since World War II.” Historical Studies in the Natural Sciences 48, no. 5 (2018): 673–84.&lt;/p&gt;&lt;p&gt;Lauer, Josh. “Plastic Surveillance: Payment Cards and the History of Transactional Data, 1888 to Present.” Big Data &amp;amp; Society 7, no. 1 (2020).&lt;/p&gt;&lt;p&gt;Light, Jennifer. “When Computers Were Women.” Technology and Culture 40, no. 3 (1999): 455.&lt;/p&gt;&lt;p&gt;Medina, Eden. Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile. Cambridge, MA: MIT Press, 2011.&lt;/p&gt;&lt;p&gt;Müller-Wille, Staffan, and Isabelle Charmantier. “Natural History and Information Overload: The Case of Linnaeus.” Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43, no. 1 (2012): 4–15.&lt;/p&gt;&lt;p&gt;O’Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown/Archetype, 2016.&lt;/p&gt;&lt;p&gt;Scott, James C. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. New Haven: Yale University Press, 1999.&lt;/p&gt;&lt;p&gt;Wiggins, Chris H., and Matthew L. Jones. How Data Happened: A History from the Age of&lt;br&gt;Reason to the Age of Algorithms. (New York: W.W. Norton, 2024).&lt;br&gt;&amp;nbsp;&lt;/p&gt;</Content>
  </RecommendedReading>
  <StudyHours Applicant="Y" Label="Study hours" Student="Y">
    <IntroText> </IntroText>
    <ScheduledHours Applicant="Y" Label="Scheduled activity hours" Student="Y">
      <ActivityHours>
        <ActivityType>Lectures</ActivityType>
        <Hours>22</Hours>
      </ActivityHours>
      <ActivityHours>
        <ActivityType>Seminars</ActivityType>
        <Hours>11</Hours>
      </ActivityHours>
    </ScheduledHours>
    <PlacementHours Applicant="Y" Label="Placement hours" Student="Y">
      <ActivityHours>
        <ActivityType></ActivityType>
        <Hours>0</Hours>
      </ActivityHours>
    </PlacementHours>
    <TotalHours Applicant="Y" Label="Independent study hours" Student="Y">
      <Hours>167</Hours>
    </TotalHours>
  </StudyHours>
  <Notes Applicant="Y" Label="Additional notes" Student="Y">
    <Content></Content>
  </Notes>
</CourseUnit>
