Towards Mutual Benefit: Reflecting on Artist Residencies as a Method for Collaboration in DIS
Bibliographic record
Abstract
While cross-disciplinary collaboration continues to be a cornerstone of inventive work in interactive design, the infrastructures of academia, as well as barriers to participation imposed by our professional organizations, make collaboration between particular groups difficult. In this workshop, we will focus specifically on how artist residencies are addressing (or not addressing) the challenges that artists, craftspeople, and/or independent designers face when collaborating with researchers affiliated with DIS. By focusing on the question “what is mutual benefit?”, this workshop seeks to combine the perspectives of artists and academic researchers who collaborate with artists (through residencies or other forms of sustained collaboration) to (1) reflect on benefits or deficiencies in what the residency research model is currently doing and (2) generate resources for our community to effectively structure and evaluate our methods of collaboration with artists. Our hope is to provide recognition of the research contributions of artists and pathways for equitable inclusion of artists as a first step towards broader infrastructural change.
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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 | 0.054 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.030 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".