Framework for Art and Relating Practice
Bibliographic record
Abstract
Art has the capacity to affect relationships—helping people express what's difficult to say, building connections across differences, challenging assumptions, strengthening community bonds, or catalyzing change. Whether you're already bringing people together with art, considering this kind of work, or curious about what art can accomplish in different contexts, this resource offers language for understanding how art serves various purposes in relational settings. This framework identifies five distinct ways art can function when it brings people together. These approaches show up in projects across communities, institutions, and collaborative spaces. By naming these patterns, this resource can help you communicate the value and intentions of your work—whether to funders, partners, participants, or yourself—or help you consider possibilities for new projects.
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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".