Bibliographic record
Abstract
reader : You have said much about civilisation – enough to make me ponder over it. I do not now know what I should adopt and what I should avoid from the nations of Europe, but one question comes to my lips immediately. If civilisation is a disease, and if it has attacked the English nation, why has she been able to take India, and why is she able to retain it? editor : Your question is not very difficult to answer, and we shall presently be able to examine the true nature of Swaraj; for I am aware that I have still to answer that question. I will, however, take up your previous question. The English have not taken India; we have given it to them. They are not in India because of their strength, but because we keep them. Let us now see whether these propositions can be sustained. They came to our country originally for purposes of trade. Recall the Company Bahadur. Who made it Bahadur? They had not the slightest intention at the time of establishing a kingdom. Who assisted the Company's officers? Who was tempted at the sight of their silver? Who bought their goods? History testifies that we did all this. In order to become rich all at once, we welcomed the Company's officers with open arms. We assisted them. If I am in the habit of drinking Bhang, and a seller thereof sells it to me, am I to blame him or myself?
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".