Bibliographic record
Abstract
reader : When you speak of driving out Western civilisation, I suppose you will also say that we want no machinery. editor : By raising this question you have opened the wound I had received. When I read Mr Dutt's Economic History of India, I wept; and, as I think of it again, my heart sickens. It is machinery that has impoverished India. It is difficult to measure the harm that Manchester has done to us. It is due to Manchester that Indian handicraft has all but disappeared. But I make a mistake. How can Manchester be blamed? We wore Manchester cloth, and that is why Manchester wove it. I was delighted when I read about the bravery of Bengal. There are no cloth-mills in that Presidency. They were, therefore, able to restore the original hand-weaving occupation. It is true, Bengal encourages the mill industry of Bombay. If Bengal had proclaimed a boycott of all machine-made goods, it would have been much better. Machinery has begun to desolate Europe. Ruination is now knocking at the English gates. Machinery is the chief symbol of modern civilisation; it represents a great sin. The workers in the mills of Bombay have become slaves. The condition of the women working in the mills is shocking. When there were no mills, these women were not starving. If the machinery craze grows in our country, it will become an unhappy land.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.228 | 0.174 |
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".