Becoming in the style of a/r/tography: A c/a/rtography of sorts
Bibliographic record
Abstract
This paper begins by asking what does becoming mean in a/r/tography? Meandering through different possibilities that a/r/tography invites, the paper becomes an opportunity for two scholars to think with the concept, how it travels, and different ways of being with it that enable us to write together. In doing so we acknowledge the importance of experimental and emergent approaches to research in art education. The authors fold in rich meanings from in and outside of the field of art education that help grow, inform, and enrich our work. We believe that a/r/tography enables us to experiment with a transdisciplinary approach to research weaving in concepts that we study through our material explorations and their theoretical situating. The process of making this paper, and the form this paper takes, resonate with the ways in which our research and how we practice as artist researchers, are complicated by ideas shared between many fields. We begin in part one with a journey through theory. Part two of this paper is documentation of the author’s c/a/r/tographic exchange. Perhaps what becomes most notably apparent, and of interest, is how we are always informed by research and concepts in the humanities.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".