interpretativity

Interpretativity refers to the quality or degree to which something invites, permits, or requires interpretation. It is used across disciplines to describe how texts, linguistic expressions, artifacts, or computational models can be read, understood, or explained, and how easily meanings can be constructed or contested. The concept overlaps with interpretability in philosophy, linguistics, and cognitive science, but may be employed more loosely to emphasize interpretive openness or ambiguity rather than technical transparency.

In philosophy and literary studies, interpretativity is linked to hermeneutics and the idea that meaning is not fixed but emerges from the interaction between text, context, and interpreter. Texts with high interpretativity allow multiple plausible readings, whereas highly determinate texts constrain interpretation. Factors that affect interpretativity include historical context, authorial intention, reader perspective, and cultural frameworks.

In linguistics and discourse analysis, interpretativity describes how utterances convey or invite interpretation beyond literal content, including implicature, stance, and presumed audience knowledge. It also relates to the way languages encode evidentiality, modality, and discourse structure, shaping interpretive possibilities.

In logic, mathematics, or computer science, the term is less standard; when used, interpretativity might refer to the ease with which a formal system or model can be interpreted or translated into meaningful concepts. In artificial intelligence, interpretativity or interpretability concerns how understandable a model’s decisions are to humans, with emphasis on explanations, transparency, and user trust.

Noting that interpretativity is not a strictly defined technical term, usage varies by field, but all formulations share an interest in what makes interpretations possible, plausible, or actionable.