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
Artificial Intelligence (AI) is the hottest topic of recent years, and not only in technology. It is everywhere – from toothbrushes to scientific articles. It consumes hundreds of billions of dollars, shakes stock markets, undermines the credibility of digital content, hallucinates and feeds apocalyptic prophecies. What is AI really? Should we understand Artificial Intelligence as Alien Intelligence, as Yuval Noah Harari suggests, or rather expect biological intelligence to merge with silicon intelligence via brain-computer interfaces, together with Raymond Kurzweil? Why does our science-based civilization suddenly turn towards black boxes and mysterious oracles? We will try to define what AI is and explain what it is not, along the way debunking a few urban legends about eavesdropping on thoughts and transferring consciousness to cyberspace. We will also discuss the real threats resulting from the fact that for years we have been giving the reign of our souls to algorithms, but we do not notice it, listening to stories about the coming “AI apocalypse".
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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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