Bibliographic record
Abstract
reader : now you will have to explain what you mean by civilisation. editor : It is not a question of what I mean. Several English writers refuse to call that civilisation which passes under that name. Many books have been written upon that subject. Societies have been formed to cure the nation of the evils of civilisation. A great English writer has written a work called ‘Civilization: its Cause and Cure’. Therein he has called it a disease. reader : Why do we not know this generally? editor : The answer is very simple. We rarely find people arguing against themselves. Those who are intoxicated by modern civilisation are not likely to write against it. Their care will be to find out facts and arguments in support of it, and this they do unconsciously, believing it to be true. A man, whilst he is dreaming, believes in his dream; he is undeceived only when he is awakened from his sleep. A man labouring under the bane of civilisation is like a dreaming man. What we usually read are the works of defenders of modern civilisation, which undoubtedly claims among its votaries very brilliant and even some very good men. Their writings hypnotise us. And so, one by one, we are drawn into the vortex. reader : This seems to be very plausible. Now will you tell me something of what you have read and thought of this civilisation?
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.061 | 0.022 |
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".