Using Topic Modeling as a Semantic Technology: Examining Research Article Claims to Identify the Role of Non-Human Actants in the Pursuit of Scientific Inventions

Actor-network theory (ANT) represents a research paradigm that emerged within science and technology studies by explicitly focusing on the contingency of scientific inventions and the role of non-human actants in the invention course of action. The article adopts an ANT perspective to focus on the invention of Sub-Wavelength Grating (SWG) photonic metamaterials by the members of a research group in the National Research Council (NRC) of Canada. The results are based on unstructured interviews with the key inventor and two domain experts as well as on textual analysis (topic modeling) of the contributions and novelty claims in the corpus of research articles by the NRC group crafting the concept and potential applications of SWGs in the photonics domain. Topic modeling is a type of statistical modeling that uses unsupervised machine learning to identify clusters or groups of similar words within a body of text. It uses semantic structures in texts to understand unstructured data without predefined tags or training data. Adopting topic modeling as a semantic technology allowed the identification of two of the key non-human factors or actants: (a) photonics design and simulations and (b) the fabrication techniques and facilities used to produce the physical prototypes of the photonics devices incorporating the invented SWG waveguiding effect. Using topic modeling as a semantic technology in ANT-inspired research studies focusing on non-human actants provides significant opportunities for future research.