Whether it’s for building the perfect chapter title, analyzing existing literature, or maybe just a personal etymological adventure, there is usefulness in providing quantitative context to words and concepts. Should such a framework exist, it should be easy-to-understand and broadly applicable for authors, students, and other individuals alike.
The Universal Concept Classification Framework (UCCF) proposed below involves five categories in which any word/concept can be scored. Each category’s score has range [0,20], spanning the full spectrum of possible values across each category. Where possible, the highest possible score for each category (20) should represent the more complex end of the spectrum (see below). The individual scores can then be summed to give a combined UCCF Score with range [0,100].
The individual category scores as well as the combined UCCF Score provide an easy way for readers and writers to understand and analyze the relative impact of certain words/concepts on readers, among other applications.
Universal Concept Classification Framework (UCCF)
- Get (Concrete=0, Abstract=20): Low scores represent words/concepts that are concrete, tangible, well-defined, and easy to understand. High scores represent words/concepts that are abstract and open to interpretation.
- Act (Controllable=0, Uncontrollable=20): Low scores represent words/concepts that are controllable, created, and/or driven by an individual, group, or machine. High scores represent words/concepts that are by nature uncontrollable.
- Dim (Independent=0, Dependent=20): Low scores represent words/concepts that are independent of other words/concepts and can stand alone for meaning and interpretation. High scores represent words/concepts that are complex, very dependent upon other words/concepts, and are often very interconnected to support interpretation.
- Set (Known=0, Changing/Unknown=20): Low scores represent words/concepts that are very well known and not subject to change in meaning or interpretation across time, language, and society. High scores represent words/concepts that change rapidly or may be universally undefined across time, language, and society.
- Rad (Plain=0, Intriguing=20): Low scores represent words/concepts that are plain and without dimension. High scores represent words/concepts that are multidimensional, mysterious, and full of intrigue.
No framework is without fault, and especially in the measurement of unstructured information, the UCCF certainly has limitations. However, it’s a quick and easy way to begin to better understand words/concepts, and I believe this type of methodology has broad applications.
One example is in the building of book titles and chapters, where authors may want to represent a broad spectrum of word types. One type of chapter may want to maximize combined UCCF Scores, others may want to keep combined UCCF Scores to a minimum, and a third type may want to have words that cover the widest range of combined UCCF Scores.
Another application may be in the analysis of certain authors, languages, or successful books in general. Do authors write about similar concepts according to the UCCF? Is there a correlation between successful books and the UCCF Scores represented by certain titles? These types of questions could be investigated using a new quantitative approach.
In general, applying simple quantitative methods to abstract ideas can provide a new way for thinking and contextualizing decisions, such as choosing book titles, and analyzing content and content creators, such as popular authors/bloggers.