Bibliographic record
Abstract
Research-creation has existed in an ethical gray-area since its introduction to the academy. In developing the Know Thyself as Virtual Reality project, we realized that the current standards for ethics review for university- based artists are not adequate for research-creation projects which tend to involve ethical concerns distinct from conventional research and art. This is particularly clear when a research creation project, like KTVR requires the use and manipulation of the personal data of others. Digital data can be useful to researchers and artists alike, but it also implies a wide variety of unique ethical concerns. While regulations and policies need to be updated for all researchers, the lack of ethical guidelines for artist-researchers compounds the risk that they face when working with personal data. In order to gain a better understanding of the implications of the growing proliferation of data, much of the focus of the KTVR project (and the content of the VR artworks) has turned to understanding emerging and evolving frameworks for the ethical use of human data in research- creation projects.
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 | 0.051 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".