The Rhetoric of (Interdisciplinary) Science: Visuals and the Construction of Facts in Nanotechnology
Bibliographic record
Abstract
Since Jeanne Fahnestock attention to the role of between images and rhetoric has grown, both generally and in rhetoric of science (Hope, 2006; Fleckenste and Richards, 2008, and others).R Alan Gross (2007; 2014) and Don Idhe (2007), among others, have responded, generating insight into the function of visuals in science research.Some of this work has visuals in specific disciplines (for example, Gross has looked at visuals in biology, chemistry, and geology); other work has explored the function of visuals across disciplines (for example, Idhe has drawn examples from medi of complexity in the relationship between visual representations and "reality").This work has enabled a better understanding of how images contribute to knowledge generation in science.While this work draws from published images from historical scientific discoveries, it also focuses on disciplinary science such as biology (including medical images) or physics.So far researchers have not explored how interdisciplinary fields in science such as nanotechnology employ visuals.The newly emerging field of nanotechnology brings together expertise from a range of areas.For example, studying prion (protein misfoldi Creutzfeldt-Jakob disease [CJD] in humans; bovine spongiform encephalitis [BSE] in mice, sheep, and cows; and chronic wasting disease [CWD] in cervids [deer, elk, and caribou] combines expertise from physics, chemistry, biology, medicine, veterinary medicine and engineering.Research on third generation solar cells requires expertise from chemistry, physics, and engineering.The methods used to answer research questions about scientific phenomena at the nanoscale (smaller than 100 nm or 10 interdisciplinary, and the data derived from these methods
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".