Bibliographic record
Abstract
editor : Your last question is a serious one, and yet, on careful consideration, it will be found to be easy of solution. The question arises because of the presence of the railways, of the lawyers, and of the doctors. We shall presently examine the last two. We have already considered the railways. I should, however, like to add that man is so made by nature as to require him to restrict his movements as far as his hands and feet will take him. If we did not rush about from place to place by means of railways and such other maddening conveniences, much of the confusion that arises would be obviated. Our difficulties are of our own creation. God set a limit to a man's locomotive ambition in the construction of his body. Man immediately proceeded to discover means of overriding the limit. God gifted man with intellect that he might know his Maker. Man abused it, so that he might forget his Maker. I am so constructed that I can only serve my immediate neighbours, but, in my conceit, I pretend to have discovered that I must with my body serve every individual in the Universe. In thus attempting the impossible, man comes in contact with different natures, different religions, and is utterly confounded. According to this reasoning, it must be apparent to you that railways are a most dangerous institution.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".