Bibliographic record
Abstract
Relational Possibilities is an immersive design that pulls together original datasets, generative artificial intelligence imagery, itch.io video games, podcast, vignettes, and curatorial statements into two distinct, interconnected virtual museums. Through a lens of community, visitors experience the shared history of African Americans in Philadelphia through 7 Black visual and literary artists and the stories of climate racism, Indigeneity, and climate change that public art and environmental histories tell. Museum visitors experience the nonlinear interactive design by participating in a digital community archive, listening to the podcast, playing the video games, exploring the data sets, and viewing the digital exhibition site that houses both virtual museums. As the seminal collaboration between Dana Reijerkerk and kYmberly Keeton (The Creative CoLab), Relational Possibilities: A Remix of Aesthetic Forms Through Indigeneity and Blackness is a meta creative digital work between two researchers, writers, and artists from different races using generative artificial intelligence. Relational Possibilities is a digital community archive data science project that explores community relations and futurist realities of Indigeneity and Blackness through artists, writers, and public art in Philadelphia. Relational Possibilities pushes the boundaries of creative information science through art and data science technologies to expose the aesthetic complexity of Black and Indigenous forms and lived experiences. Beneath these stories are the emotions, human expressions, and societal racial tensions between The Creative CoLab. As women and librarians from different races, the project explores a reciprocal partnership with self-referential elements of reflection, use of immersive digital media, and a spectrum of our personal human emotions.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.233 | 0.066 |
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".