Bibliographic record
Abstract
FROM WIGGLESWORTH TO TURING The orderings, and orderings of orderings, that go into the generation of a human body, or even of much less complex organisms, make any catalogue of catalogues or bibliography of bibliographies look like rather simplistic stuff. It is unsurprising that much enlightenment on what is happening is derived from what happens when something goes wrong. The usual major defects known as mutations have been of enormous value in genetics. Thus, rather confusingly for anyone approaching the field for the first time, many Drosophila genes are named negatively, for what happens when they are not working: fushi tarazu , Japanese for ‘not enough segments’, is working properly when the insect produces exactly enough segments; likewise for hunchback, Krüppel (German for cripple) and so forth. Also, experiments in developmental biology often involve transplanting pieces of tissue to places in an organism that Nature, with her zeal for self-organization, would not have thought of. But if perfect orderings are the ideal, Nature sometimes seems to persist in ‘going wrong’ by keeping on doing something rather sloppily. A kind of sloppiness that is potentially informative is imperfect ordering of a pattern, in which numerous parts (usually ‘spots’ on a more or less flat surface) are neither randomly distributed nor marshalled into square or hexagonal ordering, but something in between.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".