Bibliographic record
Abstract
Abstract Even if there is no objection in principle to the idea of simulating a mind inside a computer there are still practical problems, the main one being the sheer complexity of the brain. It helps that we will be starting with simpler neural systems and leaving the details to computers, but what matters most is that we find patterns in the brain’s organization to simplify our descriptions. Our role models might be the image-compression algorithms like JPEG, which exploit regularities in pictures to shrink graphics files. Grander examples are the laws of physics, which are regularities that simplify our description of the universe. But some people doubt that many such simplifying principles exist for the brain. The law-based approach that has worked so well in physics may not work in neuroscience, they say, because the brain may be its own simplest description. It may be incompressible, like a computer file that has already been zipped once and can’t be zipped any further. In this chapter I will suggest that we can learn from the example of natural selection, which has managed to zip the brain dramatically, into a self-extracting file in the form of an egg cell, though I will also urge that the egg cell’s lessons have to be interpreted with care.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.299 | 0.106 |
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".