Bibliographic record
Abstract
A traveller who combines a taste for old towns with a love of letters ought not, I suppose, to pass through “the most picturesque city in America” without making an attempt to commemorate his impressions. His first impression will certainly have been that not America, but Europe, should have the credit of Quebec. I came, some days since, by a dreary night–journey, to Point Levi, opposite the town, and as we rattled toward our goal in the faint raw dawn, and, already attentive to “effects,” I began to consult the misty window–panes and descried through the moving glass little but crude, monotonous woods, suggestive of nothing that I had ever heard of in song or story, I felt that the land would have much to do to give itself a romantic air. And, in fact, the feat is achieved with almost magical suddenness. The old world rises in the midst of the new in the manner of a change of scene on the stage. The St. Lawrence shines at your left, large as a harbour–mouth, gray with smoke and masts, and edged on its hither verge by a bustling water–side faubourg which looks French or English, or anything not local that you please; and beyond it, over against you, on its rocky promontory, sits the ancient town, belted with its hoary wall and crowned with its granite citadel. Now that I have been here a while I find myself wondering how the city would strike one if the imagination had not been bribed beforehand.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.610 | 0.173 |
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".