Commercial, Societal and Administrative Benefits from the Analysis and Clarification of Definitions: The Case of Nanomaterials
Bibliographic record
Abstract
The managerial, policy, technical and ethical decisions centred on emerging technologies are often hampered by a lack of consensus on what falls within the remit of such decisions. A lack of clarity and agreement on definitions is especially the case for nanotechnology. Given the potential of nanotechnology to underpin the next Schumpeterian economic cycle, this limitation on decision making needs to be taken seriously. Here we add to the literature by providing a pathway for decision makers to understand the nature and value of differing definitions in the important case of nanomaterials. We identified 65 relevant sources, of which 27 provided a definition of the term ‘nanomaterial’. Based on the analysis of the content of these 27 definitions, we generated an analytical taxonomy of definitions of ‘nanomaterials’ from which we constructed seven logical categories. Our analysis provides decision makers with a taxonomy to more precisely understand the diversity of definitions, thereby assisting them in their decision‐making processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".