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Record W2057031547 · doi:10.3138/ijcs.48.153

Presenting Canada to the Scientific Gaze: The <i>Handbook for the Dominion of Canada</i> and the Eccentricity of Science Tourism

2014· article· en· W2057031547 on OpenAlexvenueaboutno aff
Peter Hodgins

Bibliographic record

VenueInternational Journal of Canadian Studies · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDominionTourismDictionBureaucracyWitnessAstronomerHistorySociologyLawPolitical scienceArt historyPoetryLiteratureArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.733
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.226
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

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