INFO 200 L2
Information, Meaning, and Communication
The transcript begins with a casual reflection on retention: watching something last Friday and not fully retaining it, yet acknowledging that information has to be factual and tell us about something else.
Core idea: information is something that has to be factual and informative, and it can be transmitted from one source to another (human or not). Anything written on the Internet can be information for someone, regardless of how they choose to use it.
Distinction introduced between news/facts as separate from books/newspapers, and how information travels between sources.
Signifier and Signified
The pizza slice example: the word "pizza slice" is a signifier; the actual physical pizza slice is the signified.
Group size demonstration (oral):
The speaker asks for group size; listed responses: . This appears as a quick, possibly PI-like digit sequence and may be included as a field example or a group activity.
Assessment: a piece of information can be about something (information about), while meaning is something extracted from information (information as meaning).
Example attempts: phrases like "Fermi Arabic is about a love story" and references to culture/history in Persian are used to illustrate how information can point to cultural content, but the wording is somewhat garbled; the point is that information can convey cultural meaning beyond the raw data.
The beaker example (Thompson and Bates framing): placing a beaker in a museum with other objects creates a transfer of external meaning; the arrangement defines meaning for audiences and communicates information about history (e.g., twelfth century in the Middle East).
Information types: dry vs wet
Dry information: metadata and factual descriptors (e.g., day of object, prominence, collection, exhibition history) that do not by themselves include cultural or historical context.
Wet information: cultural/historical background provided by experts (artists, archaeologists) that adds context today to describe the object.
The National Museum of Nature and Art website is used as an example to illustrate dry information (metadata) and the potential for wet information when cultural context is added.
The term metadata is introduced; other terms like "carrier paper" are mentioned but not deeply explained.
The idea is that dry information alone does not inform us about the broader cultural/historical context; wet information enriches understanding by providing interpretive context.
Buckland's uses of information
Buckland's threefold categorization: information as thing, information as process, and information as knowledge.
Information as thing: the physical object itself (the artifact, beaker, etc.).
Information as process: the act of analyzing, extracting meaning, or interpreting the object (e.g., an archaeologist or art historian analyzing the object).
Information as knowledge: the final meaning or artwork produced after interpretation (the result of the process, including how the director or curator interprets the piece).
Illustrative chain:
Base object as physical thing → information as process when experts extract meaning → information as knowledge when the interpretation is presented (e.g., the final exhibit or narrative constructed around the object).
The process-focused view frames interpretation as a necessary step for turning data (objects) into meaningful knowledge.
Generative AI in academia: background and practice
The presenter introduces GenAI (generative AI) and outlines a case for a fair policy for using GenAI in FAST (the program/course) along with reasoning.
Common AI tools mentioned: PlexigBT, Clavvy, Gemini (and others). AI is widely accessible, including on Google searches and within Microsoft products (marketed as helpful for writing in the humanities and social sciences).
Personal experiences with AI:
The speaker avoided AI tools for about one year to refine writing; acknowledges that AI tools can be useful for some fields but not needed in others.
A brother in a different field (postdoctoral fellow in biology) uses AI extensively to refine text because the field prioritizes results over language precision.
In archival science, AI can be used to transform degraded documents into structured text for analysis, illustrating a practical, domain-specific use.
Key takeaway: despite AI facilitation, a human-in-the-loop approach remains important for interpretation and quality, especially in research and analysis.
Academic integrity, data, and practical concerns
University of British Columbia (UBC) survey observations: AI tools are convenient and sometimes free, but the service quality can degrade over time as critical thinking skills may atrophy due to overreliance; users may start paying for tools.
Potential issues with AI tools:
Bias embedded in training data
Ambiguities in terms of service
Data privacy and what happens to data uploaded to tools (e.g., access permissions, data retention)
Unclear or restricted access to personal data (e.g., Google Drive integration)
Group activity: groups will draft a guidance document addressing academic integrity and ethical AI use; guidelines should include how to proceed when in doubt, and the importance of asking questions when unsure.
Attitudes toward asking questions: some people resist asking for clarification publicly due to social dynamics, but privately asking can be preferable; policies can encourage private questions.
Personal habits and integrity strategies discussed:
Early work and time management to avoid last-minute integrity issues (roughly 80% of academic integrity cases cited as due to procrastination).
The idea of keeping records of drafting (e.g., screen recording while drafting an essay) to demonstrate origin and transparency, with a private cut-off point when sufficient progress is made.
The tension between using AI to speed up routine tasks (e.g., spelling/grammar checks) versus using AI to generate entire essays from scratch, which risks compromising integrity and originality.
Ethical considerations:
If using AI, proper referencing and disclosure are important where applicable.
The policy should emphasize the student remains the primary generator of ideas and work.
Classroom policy examples:
Some courses (e.g., CS) include an AI-use section in worksheets where students declare which tools were used, illustrating practical implementation of AI transparency in coursework.
Other courses may not have explicit AI sections, highlighting variability across disciplines.
Guidelines for academic integrity and ethical AI use (group deliverable, draft guidance)
Core principles to include in the draft:
When in doubt, ask a supervisor, instructor, or peer for clarification.
Clearly define acceptable uses of AI (e.g., assistance with proofreading, formatting, or structured data transformation) versus unacceptable uses (e.g., generating original essays or exam responses from scratch).
Require disclosure of AI tools used when it contributes to a submission or analysis.
Establish a private-question channel or process to avoid public shaming and encourage constructive inquiries.
Promote early work to minimize last-minute integrity breaches and reduce the temptation to rely on AI-generated content.
Encourage a human-in-the-loop approach, ensuring that AI outputs are reviewed, interpreted, and integrated by the student.
Address data privacy and terms of service concerns, including what data is uploaded, stored, and used by AI tools.
Include considerations of bias from training data and mitigate by cross-checking AI outputs with primary sources.
Provide strategies for documenting the origin of ideas (e.g., screen recordings, version histories) to demonstrate authorship and progression.
Practical guidelines to implement:
Start assignments early; aim to finish well before deadlines.
If using AI, specify the tool and the extent of its use in the submission.
Use AI to speed up existing tasks (like grammar checks or data organization) rather than to generate content from scratch.
Maintain a personal voice and original idea generation; AI should supplement, not replace, your thinking.
Create a reporting/responding mechanism for potential issues, and encourage colleagues to discuss concerns respectfully.
Reflection and culture:
Acknowledge that AI can influence creativity and cognitive habits; monitor for declines in critical thinking and maintain practice in independent writing and analysis.
Emphasize ethical responsibilities and the importance of upholding academic standards across disciplines.
Takeaways and real-world relevance
AI is pervasive across tools and platforms; understanding when and how to use it ethically is essential for credible scholarship.
Museums and information science distinguish between raw metadata (dry information) and contextual interpretation (wet information) to build meaningful narratives about artifacts.
Buckland's framework helps researchers conceptualize artifacts as physical objects, as processes of interpretation, and as knowledge outcomes.
Group guidelines and policy development are practical steps toward responsible AI use in education and research.
Real-world relevance includes data privacy, bias awareness, and ensuring transparency in scholarly work while leveraging AI to enhance efficiency and accuracy.
References to numbers and specific terms
Group size/sequence example mentioned:
Emphasis on or 80% for the proportion of integrity cases attributed to procrastination (as cited in the discussion).
Terms mentioned: dry information, wet information, metadata, Buckland's information types (information as thing, process, knowledge).
AI tools referenced: PlexigBT, Clavvy, Gemini (illustrative examples of AI tooling).
Concepts: signifier, signified; information vs meaning; human-in-the-loop; data privacy; academic integrity; informal vs formal guidelines.
Conclusion
The material covers foundational ideas about information, meaning, and museology, then extends to contemporary issues in AI usage in academia.
It emphasizes the need for critical thinking, clear guidelines, and ethical practices when integrating AI into scholarly work, along with practical strategies for maintaining integrity and originality.