Sensing the Ocean through the Exquisite Corpse ArtScience method
Bibliographic record
Abstract
The current interconnected crises of climate breakdown, biodiversity loss, and lack of justice and equity suggest that existing mainstream actions are inadequate and that different approaches are needed. Using a participatory action research approach, we aimed to demonstrate the potential of the asynchronous group-based “Exquisite Corpse” art process to catalyze meaningful relationships within an interdisciplinary group of ocean ArtScience practitioners. We conducted two action cycles and iterations of this process and a final action cycle of collective meaning-making. The first iteration took place in Fall 2021 with 7 participants focused on the topic of “ocean hypoxia”; the second focused on “extreme ocean events” with 10 participants in Spring 2022. The third action cycle in Spring 2023 resulted in the themes of “curiosity and connecting with people in a safe(r) space,” “semi-ambiguous seed as a catalyst,” “space for experimentation and creation,” “sharing findings and experiences,” “collective creation of connected artworks,” “finding commons and common ground(s),” “giving, receiving and opening,” “developing relationships,” and “boundaries navigating fluidity.” These themes describe the benefits that can arise from the Exquisite Corpse process on both individual and community levels of practice that could be extrapolated more widely and illustrate the potential contributions of ocean ArtScience on a systemic level. On an individual level, participants gained new scientific, artistic or personal insights, expanded their practices and deeply connected with other participants. At the community level, we argue that participatory exercises like the Exquisite Corpse process can increase relational trust, generating community-wide benefits. Additionally, activating pluralistic perspectives can help communities create more welcoming spaces for diverse participants. These emerging relationships have strengthened this budding community of practice, with further projects and opportunities growing from the endeavor. Our project also highlights how ArtScience approaches can help meet the challenges of the UN Decade of Ocean Science for Sustainable Development.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 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".