Pairs Together: A/r/tographic Learning in Relation with Visual‐Textual Propositions
Bibliographic record
Abstract
Abstract A/r/tography as a methodology of art education research emphasizes the significance of artful engagement with our subjectivity as sites of living inquiry. While the technologization of society creates increased demand for the datafication of education, a/r/tography seeks understanding with the potentiality of difference. A/r/tography as a co‐creative practice retextures analysis from a reductive tool of measurement to an ongoing process of relational knowing. Opening to relationality, emotionality, and multiplicity, analysis of ‘data' becomes an engaged process with the entanglements of lived experiences that increase in richness through collaborative participation. The process of making visual‐textual pairs as a visual proposition is such an enactment of a/r/tography where the possibility of multiple readings and collaboration holds in tension shared and subjective understanding in invitational, meaningful, and accessible ways. The method of making visual pairs juxtaposes two images to create a visual metaphor, demonstrating the pedagogical possibility of research that centres on artistic practice and objects. With visual‐textual pairing, we intentionally extend the method to enable collaborative practice. By starting with the same image and text, going out to form our individual pairs, and returning to share our completed pairs. Inquiring alongside collaborators, images, texts, and situated sites, we engage reflexively and imaginatively in how we might become with the human, non‐human, and more‐than‐human.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".