Bibliographic record
Abstract
reader : You have deprived me of the consolation I used to have regarding peace in India. editor : I have merely given you my opinion on the religious aspect, but, when I give you my views as to the poverty of India, you will perhaps begin to dislike me, because what you and I have hitherto considered beneficial for India no longer appears to me to be so. reader : What may that be? editor : Railways, lawyers and doctors have impoverished the country, so much so that, if we do not wake up in time, we shall be ruined. reader : I do now, indeed, fear that we are not likely to agree at all. You are attacking the very institutions which we have hitherto considered to be good. editor : It is necessary to exercise patience. The true inwardness of the evils of civilisation you will understand with difficulty. Doctors assure us that a consumptive clings to life even when he is about to die. Consumption does not produce apparent hurt – it even produces a seductive colour about a patient's face, so as to induce the belief that all is well. Civilisation is such a disease, and we have to be very wary. reader : Very well, then, I shall hear you on the railways. editor : It must be manifest to you that, but for the railways, the English could not have such a hold on India as they have.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 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".