Bibliographic record
Abstract
When Victoria was young specialists had not been invented — the Family Doctor did you all over. You did not have a special doctor for each part. Dr. Helmcken attended to all our ailments — Father's gout, our stomach-aches; he even told us what to do once when the cat had fits. If he was wanted in a hurry he got there in no time and did not wait for you to become sicker so that he could make a bigger cure. You began to get better the moment you heard Dr. Helmcken coming up the stairs. He did have the most horrible medicines — castor oil, Gregory's powder, blue pills, black draughts, sulphur and treacle. Jokey people called him Dr. Heal-my-skin. He had been Doctor in the old Fort and knew everybody in Victoria. He was very thin, very active, very cheery. He had an old brown mare called Julia. When the Doctor came to see Mother we fed Julia at the gate with clover. The Doctor loved old Julia. One stormy night he was sent for because Mother was very ill. He came very quickly and Mother said, “I am sorry to bring you and Julia out on such a night, Doctor.” “Julia is in her stable. What was the good of two of us getting wet?” he replied. From Emily Carr, “Doctor and Dentist,” in The Book of Small, Clarke, Irwin & Company, 1942.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".