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
We are constantly being warned that our personal data is vulnerable, that it is being used and abused by artificial intelligence, giant tech corporations and controlling governments. But do we really understand what "our data" consists of and what can be done with and to it? Is it possible to unravel the complex entanglements of data gathering and processing technologies in order to see and understand our data in a meaningful way? My Data Body is a virtual reality (VR) artwork that brings together some of our most personal and sensitive data such as medical scans, social media, biometric and social security data in an attempt to make visible and manipulable our many intersecting data corpuses so that they can be held, inspected, dissected and played with as a way to start understanding and answering these questions. My Data Body has been created as part of the interdisciplinary project Know Thyself as a Virtual Reality (KTVR), a multi-faceted project that explores the ethics and aesthetics of the contemporary "data body". KTVR brings together researchers across the arts and sciences, to innovate new creatives methodologies, educational resources and ethical guidelines for working artistically with personal data.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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