Bibliographic record
Abstract
Upon entering the School of Engineering from the practice-based Faculty of Fine Arts questions arose regarding the pedagogy of design theory versus the design work of first year engineering students. Fine Arts, in the past couple of years, has tried to enter higher levels of academia by starting to offer PhD programs that are practice-based. Often times these degrees have an aspect of theorizing practice into analysis and thereby equate the process of creating art to the process of scientific experimentation. If one looks at the area of scientific inquiry, which is according to Heidegger: ‘nothing less than the making secure of methodology over what ever is (nature and history)’, questions arise over whether theorizing practice into analysis is the right approach. By doing the latter, one calls into question whether practice by itself is valid and whether by creating a (somewhat) artificial analysis one diminishes the process of practice to a secondary activity. This paper will start by looking at research done in the area of Fine Arts to solve the ‘theorizing practice into analysis’ question by creating a situation where practice can be considered as more than mere experimentation. In addition there will be a discussion as to how these findings might be applied to Engineering Design.
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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.066 | 0.018 |
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".