Bibliographic record
Abstract
reader : I now understand the lawyers; the good they may have done is accidental. I feel that the profession is certainly hateful. You, however, drag in the doctors also, how is that? editor : The views I submit to you are those I have adopted. They are not original. Western writers have used stronger terms regarding both lawyers and doctors. One writer has likened the whole modern system to the Upas tree. Its branches are represented by parasitical professions, including those of law and medicine, and over the trunk has been raised the axe of true religion. Immorality is the root of the tree. So you will see that the views do not come right out of my mind, but they represent the combined experiences of many. I was at one time a great lover of the medical profession. It was my intention to become a doctor for the sake of the country. I no longer hold that opinion. I now understand why the medicine men (the vaids) among us have not occupied a very honourable status. The English have certainly effectively used the medical profession for holding us. English physicians are known to have used the profession with several Asiatic potentates for political gain. Doctors have almost unhinged us. Sometimes I think that quacks are better than highly qualified doctors. Let us consider: the business of a doctor is to take care of the body, or, properly speaking, not even that.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.041 |
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".