Bibliographic record
Abstract
Abstract The dream of creating an artificial mind goes back at least to Rabbi Loewe, who lived in Prague in the 16th century. According to legend, the rabbi fashioned a human-shaped statue called a golem out of the mud of the Moldau river and animated it with a sort of software in the form of a text written in Hebrew on a slip of paper inserted under the creature’s tongue.9 Another pioneer in this field was Dr Frankenstein, but he assembled his creature from prefabricated parts dug out of grave yards, which is cheating. Rabbi Loewe’s closest modern successors may be the roboticists, who build their creations out of metal and electronic parts. Brain theorists pursue the same dream in a slightly different way, building our artificial minds inside computer simulations instead of in hardware. This is mainly a matter of convenience. When we want to try out a new idea, we reach for a computer keyboard and mouse, not a blow torch and safety glasses. Development goes faster, and if our creation turns evil and runs amok, we switch off the computer rather than hunting down the creature through the sewers of Prague, as Rabbi Loewe had to do.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".