Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an immersive viasual experience, Mrs. Rajko asks: do we use our technology, or is it using us?\n\nJessica Rajko is an interdisciplinary artist exploring the embodied, corporeal, and lived experience of data. As an assistant professor at Arizona State University, her work blends praxis and scholarship from dance, somatic practices, phenomenology, and human-computer interaction design. She is a founding co-Director of the ASU Human Security Collaboratory, a non-departmental collective of artists and scholars addressing complex problems affecting the security of individuals and communities, with a special emphasis on digital technologies and their uses. Considering issues such as digital civil rights and equity in tech, her research aspires to integrate intersectional feminist frameworks within all her practices. Jessica has presented and performed in various collaborative artworks nationally and internationally, including Torontoâs Scotiabank Nuit Blanche festival and New York Cityâs Gotham Festival at The Joyce Theatre. She was named one of Phoenix New Timesâs â100 Creatives of 2016.â She is the co-founder and co-director of urbanSTEW (urbanSTEW.org), a non-profit arts collective that creates participatory, art/tech installations to engage local communities in multisensory, felt experiences. Jessica received her MFA in Dance and Interdisciplinary Digital Media at Arizona State University in 2009 (outstanding graduate of the year) and her BA in Dance and Psychology at Hope College in 2005.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it