Dissecting My Data Body
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.
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 | 0.040 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.060 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".