Bibliographic record
Abstract
In her 1927 essay ‘Street Haunting: A London Adventure’, Virginia Woolf wanders into a second-hand bookshop on Charing Cross Road. A disorganised stack of volumes piled on the floor offers a panoramic view of the print culture of the previous century. Although scholars of the nineteenth century have long thought of the novel as the dominant literary form of the period, Woolf suggests that it is another category —the travel book —that overwhelms all others. There are travellers … row upon row of them, still testifying, indomitable spinsters that they were, to the discomforts that they endured and the sunsets they admired in Greece when Queen Victoria was a girl. A tour in Cornwall with a visit to the tin mines was then worthy of voluminous record. People went slowly up the Rhine and did portraits of each other in Indian ink, sitting reading on deck beside a coil of rope; they measured the pyramids; were lost to civilization for years; converted negroes in pestilential swamps. This packing up and going off, exploring deserts and catching fevers, settling in India for a lifetime, penetrating even to China and then returning to lead a parochial life in Edmonton, tumbles and tosses upon the dusty floor like an uneasy sea, so restless the English are, with the waves at their very door. 1 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.014 |
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".