Bibliographic record
Abstract
Awareness of our environments both external and internal are in continuous flux and highly mediated by technologies we have created. The research project we shall discuss is situated at the pivotal point of reframing our perception and consciousness in the context of the current in silico culture. More to the point, we shall refer to inroads into the bi-modal computer-generated simulations of blood flow patterns, in the pathological situation of a brain aneurysm, leading not only to the understanding of the phenomenon but also to the optimal communication of its complexity. This new approach, removed from both in vivo as well as in vitro, brings together visual and sound artists, computer engineers, designers and cognitive scientists with the essential goal of restructuring and re-configuring our understanding of self. This being said, the need to create ways that allow remodeling and reframing our perceptions through easily interactive tools that are also increasingly autonomous is at the core of the research we shall be presenting. Through novel approaches and complex technological mediation, the accompanying conscious experiences co-evolve and develop in complexity. The aim is for this project to serve as an example and starting point for stimulating debate.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.076 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".