Generating Captain Cook and Paul Kane into Published Authors: Case Studies of a Book History Model for Exploration and Travel Writing (pp 7-56)
Bibliographic record
Abstract
As more and more research occurs into published English-language travel literature, the production of individual texts, rather than their authorship alone, demands attention. Both the unreliability of the text as entirely the traveller's or explorer's own and the question of whether or not the text narrates only his own experience and observations have made problematical the matter of interpretation. Recently, Percy G. Adams has shown in Travel Literature and the Evolution of the Novel (1983) how, because travel writing issued often enough from writers who did no more than move around in an armchair, this genre and the novel grew indistinguishable for a time in the early eighteenth century. From then on, travel literature would often exemplify a more complex, or at least less straightforward, relation between experience and language than one might expect.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.027 | 0.025 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".