Provides a limited language sample or a sample wholly spoken from notes.
Solutions to the inference problem are evolved by optimizing the parameters of a covering grammar for a given language sample.
Drawing inspiration from the way children learn languages, we propose here a corpus and entropy-based methodology that follows a subjectivist approach to granulate and identify statements of consequence in a natural language sample, all within the periphery of a particular context.
In this study, we settled for the mid-approach; translation and back translation in addition to testing the translated instrument in a target language sample.
A tin ear for language (sample: "The loggerhead at which science and law now abut may become a tipping point") and a smattering of minor but annoying factual errors, like misidentifying a few medical experts, do not help.
Notes: The Taleist survey polled 1,007 people, a self-selecting English language sample which may or may not be representative of the overall self-publishing community.
Due to a host of reasons (culture, language, sample selection criteria, etc)., the same test items might have different parameters across cohorts.
