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Record W2750682767 · doi:10.1080/02722011.2017.1370719

Finding Thoreau in French Canada: The Ideological Legacy of the American Revolution

2017· article· en· W2750682767 on OpenAlexaboutno aff
Patrick Lacroix

Bibliographic record

VenueThe American Review of Canadian Studies · 2017
Typearticle
Languageen
FieldComputer Science
TopicThoreau and American Literature
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyYankeePower (physics)NationalityInterpretation (philosophy)DepictionHistoryLawSociologyPolitical sciencePoliticsArtLiteraturePhilosophy

Abstract

fetched live from OpenAlex

Henry David Thoreau’s Yankee in Canada is easily overlooked. Because it is so selective in its depiction of life in the St. Lawrence River valley, historians of mid-nineteenth-century Canada have shown little interest in Thoreau’s first-hand account. To American readers, it offers little of the characteristic Thoreau found in Walden and Resistance to Civil Government. Yet, it is highly significant as an expression of national self-definition. Thoreau borrowed themes at least as old as the American Revolution when noting the pernicious rule of Catholic and British power in Canada. He set out to expose the promise of republican values by emphasizing the contrast between these and the poor and morally stunted life under Old World institutions. His work must therefore be interpreted as a call to his audience to commit more deeply than ever to the ideals that animated the Great Republic’s founding moment. It must also stand as a civic interpretation of American nationality at a time when this perspective was waning. Before long, Old World peoples would be racialized and the ideological embrace of the republican values advanced by Thoreau would no longer suffice in making American citizens.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.159
Threshold uncertainty score0.975

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.010
Science and technology studies0.0270.021
Scholarly communication0.0140.004
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.034
GPT teacher head0.311
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe American Review of Canadian StudiesSame topicThoreau and American LiteratureFrench-language works237,207