The Transnational Turn: Rediscovering American Studies in a Wider World
Bibliographic record
Abstract
Few American writers have been so rooted in a single place as Henry David Thoreau. Born in Concord, Massachusetts, sixteen miles west of Boston, Thoreau spent nearly all his short life, some forty-four years, in the vicinity of his native town – “the most estimable place in all the world” he deemed it – with only brief sojourns beyond New England. Like many of his contemporaries, he did try out the big city, living close to Manhattan in 1843, an aspiring writer, age twenty-six, with hopes of a literary career. But he quickly recoiled from the urban scene. “I don't like the city better, the more I see it, but worse,” he wrote Ralph Waldo Emerson. “I am ashamed of my eyes that behold it. It is a thousand times meaner than I could have imagined. … The pigs in the street are the most respectable part of the population.” Homesick, he was back in Concord within six months. Only once did he stray outside the United States, for a week-long excursion to Montreal and Quebec. To this “Yankee in Canada,” it was a disappointing jaunt. “What I got by going to Canada was a cold.” Thoreau was simply happiest in his hometown, where he “traveled a good deal,” exploring the ponds, woods, and fields, observing and provoking the neighbors, and transforming his chosen ground, in Walden and in his journals, into a sacred site on the American literary landscape. Concord, he declared, is “my Rome, and its people … my Romans.”
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.034 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".