Honor Coding: Analogies and Algorithms in PlagiarismDetection Elizabeth Losh University of Californi - PowerPoint PPT Presentation

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Title: Honor Coding: Analogies and Algorithms in PlagiarismDetection Elizabeth Losh University of Californi


1
Honor CodingAnalogies and Algorithms in
Plagiarism-Detection Elizabeth LoshUniversity
of California, Irvine
2
  • How does the meaning of these words change with
    the advent of electronic distributed networks?
  • Academic Labor
  • Academic Freedom
  • Academic Honesty
  • And these more generally . . .?
  • Labor
  • Freedom
  • Honesty

3
  • The current two cultures problem
  • The culture of knowledge
  • The culture of information

4
Metal Detectors

5
Drug Tests

6
  • Metal Detectors at Airports
  • Purpose is primarily detection
  • Operates in public space
  • Applies to all equally
  • Labor is lessened by the machine
  • Guilty parties are manifestly guilty
    (terrorists)
  • Drug Testing
  • Purpose is primarily diagnosis
  • Operates in private space
  • Objectifies one party and elevates the other to
    expert
  • Labor is created by the machine
  • Possibility of false positives

7
Fingerprint Matching

8
Radar Screens

9
Why is Tool Literacy dominating the debate?

10
Rebecca Moore Howard on Patchwriting

11
How do we judge the most artful fabricator?

12
What about the academic labor of students?
13
What does academic credit or a university diploma
signify?
14
The Honor Code Approach
15
Is this the best way to learn?
16
Does it recognize the social dimension of
literacy and the difficulties of negotiating
public/private distinctions?
17
In teaching composition, do we want to emphasize
practices or principles?
18
What does honor mean in our culture?
19
  • What does a rhetoric of integrity suggest?
  • In relationship to diversity
  • In relationship to
  • political correctness

20
  • What happens to the rift between the two
    cultures?
  • The culture of knowledge
  • The culture of information

21
  • The choice to license turnitin.com

22
Making deals with proprietary technologies

How do universities create certain kinds of
winners and losers and legitimate certain
intellectual property regimes?
23
Thinking more about software . . .
Is this, as Ted Nelson might say, a legacy of the
cut-and-paste algorithm itself? How do our
choices about document production software have
resonance when the rules are made in places like
Xerox, Microsoft, and Google?
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