Artists Embedded in Government: Expanding the Cultural Policy Toolkit
Bibliographic record
Abstract
Artists have long worked in fields that are not related to art, instigating innovative ways of working in different institutional contexts from health to business to technology. Civic systems are no exception, with artists spearheading new ways of operating within government agencies. Some artists work covertly, bringing creative methods to traditional civic roles, while others are invited into government agencies in temporary positions as artists in residence addressing specific concerns. As local governments are increasingly challenged to provide services and operate equitably amid eroding public trust, opportunities for cross-sector collaboration uniting artists with government staff are compelling mechanisms for them to cocreate the necessary conditions for systemic change. The research reported herein explores how artists are engaged in cross-sectoral collaborative models of cultural policy within local governments. Governments already recognize the potential of formal artist residency programs conducted over set periods of time to advance civic goals. In addition, artists have been engaged in this work through informal government partnerships in departments including transportation, parks, and public health. Some collaborations act as applied research enacted internally, bringing more complex understandings of government operations, while others become deep processes of external engagement to expand awareness of local concerns. This paper presents a framework categorizing the structures in which artists work within government agencies to advance civic goals. The framework is based on research conducted on artists who were embedded in the government across the US from 2020 through 2022. Describing these structures highlights new ways of working within local government that center artists as agents that promote change; this paper lays the foundation for understanding how they operate and delineates opportunities and challenges for the artists and the governments that initiate collaboration. The framework is a foundation for cultural policy makers to evaluate artist in residence in government programs as a cultural policy tool and enable evaluation of the impact of their implementation.
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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 | 0.039 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.026 | 0.038 |
| Scholarly communication | 0.031 | 0.025 |
| Open science | 0.006 | 0.050 |
| Research integrity | 0.009 | 0.014 |
| 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".