Mobilizing the “Multimangle”: Why New Materialist Research Methods in Public Participatory Art Matter
Bibliographic record
Abstract
This research study is focused on new materialist modes of social inquiry and in particular the material-discursive practices that situate all bodies, human and nonhuman, in relations of matter and mattering. The present study investigates the work of the Mobile Art Studio (MAS), a transitory creative research lab that brings participatory art into pub-lic space to develop greater community engagement with issues of social justice. We explore MAS's recent performance, Reconstruction (2016), as a case study for new materialist arts-based methodologies that decenter the human as an exclusive maker of meaning, shifting instead to focus on the relationships between humans, lived spaces, and creative media. We situate this discourse within Leisure Studies as it is a vital site for developing a broadly interdisciplinary scholarly conversation on arts-based practi-ces oriented toward the public good. The paper first outlines how Reconstruction (a sculptural, mixed media, sitespecific, participatory, screen-based project) can elucidate posthuman understandings of the subject/object divide as a multimangle. That is, Reconstruction's research design and collected data reveals a research space constructed through the contingent relations between public architecture, the performance installation, the art materials, and participants. These relations offer an understanding of space as agential, containing multiple temporalities, materialities, and affective resonances. We then argue that creative intra-actions with such spaces produce knowledges that are scarcely, if at all, represented in the academy.
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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 | 0.351 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.135 |
| Scholarly communication | 0.030 | 0.056 |
| Open science | 0.009 | 0.037 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".