Commercial, Societal and Administrative Benefits from the Analysis and Clarification of Definitions: The Case of Nanomaterials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it