Bibliographic record
Abstract
Integrating artistic practice into STEM to create STEAM offers learners intersubjective experience with subjects they study.This contrasts with traditional STEM education which upholds an ontological belief in human separation from the world they observe.This ontological separation justifies the absence of ethical considerations towards those subjects we study.In the context of our environmental crisis, this is problematic because humans are neither separate nor free from ethical ties to those we study.Rather, we are dependent on those who spark our curiosity and sustain our lives.Integrating artistic practices such as oil painting into STEM education brings intersubjectivity into scientific learning.Creating art depends on treating our affective and embodied responses to subjects as valid data and sources of knowledge.This brings learners into dialogue with fellow subjects and shifts from a traditionally objectifying stance towards one of kinship.The author demonstrates her embodied dialogue with a wild doe through various stages of painting an image of the doe onto plywood.Her creative process shows a participatory relationship arising between herself as researcher and the doe, her fellow subject.Throughout the painting's development, diverse subjectivities emerge, including the doe, the plywood and the author, creating a participatory collaboration and an intersubjective experience.This article draws on David Abram's and Goethe's theories of participatory observation and embodied knowledge of 18 th -century artisans.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".