Bibliographic record
Abstract
This article analyzes the capacity for public art to build a “métis” infrastructure (Grabill 2007) capable of supporting local experiential and performative knowledge about the environment. The article describes the work of UPPArts, a small, nonprofit arts organization focused on promoting environmental awareness. Their long-term cultivation of partnerships with state agencies, NGOs, and community residents resulted in a robust collaborative arts program that engaged the public in making “nonexpert” (Simmons and Grabill 2008) knowledge based on the embodied experience of living within a contaminated urban watershed. Using field research conducted over the course of the author’s work with the organization, the article presents a thick description and rhetorical analysis of UPPArts’ annual culminating event, a parade known as the Urban Pond Procession. The article argues that the representation and performance of community knowledge in the form of community-made arts projects like the Urban Pond Procession helped mobilize a community into a public that could advocate for its right to environmental remediation and protection. The lesson of UPPArts is that the material dimensions of artistic method matter. The close attention that art-making forces us to pay to how we use materials to make things with each other can reconfigure social relations around the idea of a watershed as a rhetorical common-place (Druschke 2013).
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".