Not a long note or a detailed dissection, but just a reminder: whenever you take single-dimensional data and make it multidimensional, it becomes harder and more subjective to analyze it. (EDIT: To clarify, you can represent multidimensional data multidimensionally. But mapping multidimensional data to a lower-dimensional space usually involves finding a fit, which can introduce error. Mapping it to a lower-dimensional space is usually an important step in explaining it.) I suspect you’ll find that if you have this many dimensions for people to respond by, you’ll get lots of different-looking representations of the same underlying signal.
Maybe that’s not bad: the default sort order is newest-to-oldest—basically arbitrary—and for most cases, “generally positive” and “generally negative” signal will be sorted in the correct order. But I still feel some suspicion because it’s just one UI feature and it took you about two pages of words to pitch it.
Not a long note or a detailed dissection, but just a reminder: whenever you take single-dimensional data and make it multidimensional, it becomes harder and more subjective to analyze it. (EDIT: To clarify, you can represent multidimensional data multidimensionally. But mapping multidimensional data to a lower-dimensional space usually involves finding a fit, which can introduce error. Mapping it to a lower-dimensional space is usually an important step in explaining it.) I suspect you’ll find that if you have this many dimensions for people to respond by, you’ll get lots of different-looking representations of the same underlying signal.
Maybe that’s not bad: the default sort order is newest-to-oldest—basically arbitrary—and for most cases, “generally positive” and “generally negative” signal will be sorted in the correct order. But I still feel some suspicion because it’s just one UI feature and it took you about two pages of words to pitch it.