Bibliographic record
Abstract
Abstract Guided by this special issue theme of ‘Curating as a Condition of Art Education’, I will discuss how curating, and art production more broadly, increasingly operate directly as expanded educational praxes. The educational turn in professional curatorial and art practice has been prompted by consideration of pedagogical models within various curatorial strategies and critical art projects. In this article I speculate on curatorial practice as a form of artistic investigation by art students. My research engages with theories of public curation, the artist-curator, and material culture art education, and I illustrate this discussion by presenting a case study of a collaborative art project initiated in Canada’s largest education archive. By enfolding theory and practice, I was able not only to envision and implement a project with students, but also to reorient my role from art teacher to ‘teacher-curator’ and address the non-traditional pedagogic thinking required to facilitate these kinds of projects at the high school level.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".