Bibliographic record
Abstract
reader : You tell me that, when two men quarrel, they should not go to a law court. This is astonishing. editor : Whether you call it astonishing or not, it is the truth. And your question introduces us to the lawyers and the doctors. My firm opinion is that the lawyers have enslaved India, and they have accentuated the Hindu–Mahomedan dissensions, and have confirmed English authority. reader : It is easy enough to bring these charges, but it will be difficult for you to prove them. But for the lawyers, who would have shown us the road to independence? Who would have protected the poor? Who would have secured justice? For instance, the late Mr Manomohan Ghose defended many a poor man free of charge. The Congress, which you have praised so much, is dependent for its existence and activity upon the work of the lawyers. To denounce such an estimable class of men is to spell justice injustice, and you are abusing the liberty of the press by decrying lawyers. editor : At one time I used to think exactly like you. I have no desire to convince you that they have never done a single good thing. I honour Mr Ghose's memory. It is quite true that he helped the poor. That the Congress owes the lawyers something is believable. Lawyers are also men, and there is something good in every man.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.021 |
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".