This is not a Pipe: Incorporating Art in the Science Curriculum
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
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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