Bibliographic record
Abstract
Dr. Helmcken's office was a tiny two-room cottage on the lower end of Fort Street near Wharf Street. It sat in a hummocky field; you walked along two planks and came to three steps and the door. The outer room had a big table in the centre filled with bottles of all sizes and shapes. All were empty and all dusty. Round the walls of the room were shelves with more bottles, all full, and lots of musty old books. The inner office had a stove and was very higgledy-piggledy. He would allow no one to go in and tidy it up. The Doctor sat in a round-backed wooden chair before a table; there were three kitchen chairs against the wall for invalids. He took you over to a very dirty, uncurtained window, jerked up the blind and said, “Tongue!” Then he poked you round the middle so hard that things fell out of your pockets. He put a wooden trumpet bang down on your chest and stuck his ear to the other end. After listening and grunting he went into the bottle room, took a bottle, blew the dust off it, and emptied out the dead flies. Then he went to the shelves and filled it from several other bottles, corked it, gave it to Mother and sent you home to get well on it. He stood on the step and lit a new cigar after every patient as if he was burning up your symptoms to make room for the next sick person. 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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 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".