Bibliographic record
Abstract
Go Southwest, Old Man, a sort of personal remake of 'Go West, Young Man', the founding episteme of the American nineteenth century, conciliates these two souls (well, not to be pretentious, let's simply say two sides) that have actually always lived in harmony. This is a book generated by a quarter of a century spent wandering around the canyons and deserts of Arizona, Colorado, Utah and, above all New Mexico, with a view to penetrating the by now universal legend of the West, approaching the cultures (English, Hispanic and native American), and mastering the literature. The slant is composite: melding the scholarly with the informative and the travel journal, and the writing is composite too, because the book speaks English and Italian. It talks about cinema (lots of John Ford) and about detective stories, the most popular genre here, about visual arts and Latino folklore, about the legend of the West, the so-called 'Soul of the Southwest', and the kitsch style of Santa Fe. And it talks about (and with) some of the greatest writers that the Southwest has spawned: Rudolfo Anaya, Stanley Crawford, John Nichols and Hillerman. So what we have is a first-hand experience of the Southwest; where the ego is not entrenched within a precise disciplinary role but opens up – and exposes itself – to the thrilling risk of the discovery that can renew it.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.104 | 0.042 |
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".