A woman in Canada
Bibliographic record
Abstract
I believe that the average Englishman keeps a small but warm corner of his heart for the word “colonies.” Pride of possession counts for nearly all the warmth in that corner. When he looks there he finds a few vague notions lying loose, just anyhow, all warm, all prized in a careless, happy way; but none of them loved in laborious detail. The vague notions spell vague things to him. India generally spells, I think, “Elephants a-pilin’ teak,” and whisky-pegs; Africa, diamonds and “Kaffirs”; Australia, sheep and cricket; Canada, wheat and discomfort. It sounds foolish and almost impossible, but I believe that for the average Briton that is a fairly accurate description of what the Colonies amount to. The word “ Canada ” brings to his brain pictures of Liverpool receiving vast cargoes of wheat and distributing them over the country at a lower price than the home farmer demands. It also arouses dim visions of privations endured most impatiently by sundry of his friends who have gone out to Canada to settle, and hurried back incontinently because the young country did not contain all the comforts of the old. The name of Canada is to average Englishmen an empty word—as a nation we do not realize her beauty, her power, or her proud resentment of our ignorance of both.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.051 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".