This is not a Pipe: Incorporating Art in the Science Curriculum
Bibliographic record
Abstract
Science courses employ instructional strategies that are based on lecture, drill, and practice to help students memorize collections of facts and procedures of increasing complexity. These strategies emphasize the acquisition of knowledge through the development of logical-mathematical skills employed in problem solving and verbal-linguistic abilities to make sense of the concepts and jargon in the field. Due to its highly abstract character, these science courses deal with complex representations that require an understanding of the role of mental models. Learners need to develop their visual-spatial skills as a means of gradually acquiring visual literacy while grappling with the symbols and conventions displayed in the figures, diagrams, and charts in textbooks. The Art & Science Project started at Vanier College as part of the History of Science course in the liberal arts program and was later adapted for use in three core chemistry courses (General, Solution, and Organic Chemistry) in the science program. The project uses a cross-disciplinary integration between visual arts and the natural sciences to promote a deeper understanding of the role of models. The liberal arts students analyze the parallels between the evolution of modern scientific concepts and the art movements from the same historical periods. Science students create visual representations that portray core ideas and threshold concepts in the field. The goal is to portray these abstractions using visual arts as means of creating meaning through symbolic visual representations while developing new perceptions of visual forms.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".