It’s the slowest week of the year

GPT Agents

• Working on getting extended “trimmed” data out of the model
• Had an extensive set of talks with Stacey about using the twitter dataset to support a qualitative study of the trained model of COVID data. The thing that finally clicked was my description of the model as analogous to someone who has read every one in the data set. Such a person could more-or-less repeat actual tweets in a way that would reflect the underlying frequency, but they could also synthesize knowledge. For example, we were using probes like “Dr. Fauci is “, which can also be found in the database. But the phrase “Dr. Fauci is like a ” does not appear anywhere. But the model has no problem with it. 2 of the responses in the first test of 15 results say Dr. Fauci is like a “president”, which makes a lot of sense, actually
• Working on getting the date info out. Everything works, but it doesn’t really make more text. The system has a sense of how long a tweet should be and how they end, it seems
• Getting up to speed on NVivo
  • Intro & Import: A general overview of what NVivo does, and how to import data.
  • Organize: A first look at how to code, note-keeping options, how to create cases (the units of analysis) and give them attributes (descriptive information).
  • Explore: Run a word frequency query and create a word cloud, then create a simple chart showing opinions on an issue.