Presenting Canada to the Scientific Gaze: The <i>Handbook for the Dominion of Canada</i> and the Eccentricity of Science Tourism
Bibliographic record
Abstract
Abstract: In 1884, the Handbook for the Dominion of Canada was produced by the Montreal organizing committee for the first-ever meeting of the British Association for the Advancement of Science to be held outside of the British Isles. At first glance, the Handbook resembles present-day tourist guides in that it separates the country into sections, provides historical and natural historical descriptions for each, and identifies major sights. Yet its style blends scientific and bureaucratic diction, it is almost completely shorn of affective qualities, it contains few illustrations but an excess of statistical and scientific information, and it proposes what would seem to us to be erratic itineraries through places such as mines, mental asylums, factories, or waterworks. This article uses the Handbook to think about tourist handbooks as forms of middlebrow mediation of touristic experience that draw heavily on the emergence of bureaucratic and scientific diction to establish their cultural authority. It questions conventional constructions of nineteenth-century Canadian tourism in terms of the Romantic desire for the untamed, the exotic, and the sublime. Furthermore, it suggests that the Handbook was written in hopes of enrolling the visiting British scientists into a larger project of encouraging British immigration to Canada.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.039 | 0.023 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".