Bibliographic record
Abstract
reader : I have now learnt what the Congress has done to make India one nation, how the Partition has caused an awakening, and how discontent and unrest have spread through the land. I would now like to know your views on Swaraj. I fear that our interpretation is not the same. editor : It is quite possible that we do not attach the same meaning to the term. You and I and all Indians are impatient to obtain Swaraj, but we are certainly not decided as to what it is. To drive the English out of India is a thought heard from many mouths, but it does not seem that many have properly considered why it should be so. I must ask you a question. Do you think that it is necessary to drive away the English, if we get all we want? reader : I should ask of them only one thing, that is: ‘Please leave our country.’ If after they have complied with this request, their withdrawal from India means that they are still in India, I should have no objection. Then we would understand that, in our language, the word ‘gone’ is equivalent to ‘remained’. editor : Well, then, let us suppose that the English have retired. What will you do then? reader : That question cannot be answered at this stage. The state after withdrawal will depend largely upon the manner of it.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".