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
Bibliographic record
Abstract
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 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 allows identifying 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".