I was particularly pleased that you included the “narrative filling the gap between qualifications or assessment and what is claimed” in the competence component. My research in related areas suggest that this is much more important than has traditionally been understood. I would suggest that you also include conceptual scenarios underlying the definition of the competence. I use the concept of conceptual frame that seems to pop up in a variety of disciplines from anthropology (Goffman) and linguistics (Fillmore) to artificial intelligence (Minsky) and policy analysis (Schon). I particularly like the model proposed by George Lakoff as Idealized Cognitive Models that allows you to link propositional and figurative as well as verbal and imagistic content together.

This might make it possible to take into account process-based models of curriculum (such as that championed by Stenhouse).

I have a word of caution, however.

I really like your distinction between the conceptual model and information model. Too often these are assumed to be one and the same. But even if they’re not, there is a danger of applying the descriptive algorithmic apparatus of the information model to the conceptual model and ending up assuming that the information model actually is the same as the conceptual model. This happened with research in AI where researchers actually ended up believing that our intelligence is truly algorithmic and in my view set AI research back by 20 years.

I see the same danger with ontologies used by information models such as RDF. They are fine as long as they are thought of as purely descriptive or representational conventions but will break down as soon as we try to establish one-to-one identity relations with the conceptual models. This is because conceptual models consist of multidimensional, fuzzy and non-algorithmic elements and could even be thought of as processes rather than entities, whereas information models are almost the exact opposites. They can approximate some of the properties of conceptual models but if they could replicate them exactly they would not be computationally viable. For example, predicates like ‘is a’ or ‘kind of’ are subject to prototypicality effects in human conceptual models (robin is more of a bird than ostrich, Ford Mondeo is more of a car than Ka or Hummer, etc.). The information model removes this element for good reason but it’s a computational and representation reason internal to the information model not in any way reflecting some properties of the conceptual model.

There could be some benefit in embracing the fuzziness of conceptual models, though. I always point to Helen Keller’s ‘Century of the Gene’ as an example of how conflicting, imprecise definitions (even with concepts central to the inquiry) can be beneficial.

Finally, I love the idea of doing one to one clarification conversations following a bigger meeting. I’m going to see if I can implement it in some of my projects.

Sorry for being so wordy.