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Record W2294875549 · doi:10.3138/chr.3035

High Modernism, Conflict, and the Nature of Change in Canada: A Look at <i>Seeing Like a State</i>

2016· article· en· W2294875549 on OpenAlexvenueaboutno aff
Tina Loo

Bibliographic record

VenueCanadian Historical Review · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyModernism (music)Modernization theoryState (computer science)Power (physics)PoliticsPeriod (music)NarrativeSociologyEnvironmental ethicsSocial changeAestheticsHistoryPolitical scienceLawLiteraturePhilosophyArt

Abstract

fetched live from OpenAlex

Seeing Like a State (1998), anthropologist James C. Scott's critique of twentieth-century development and the ideology of “high modernism” that animates it, has shaped the writing of Canadian history, as it has in so many other fields. Environmental historians of Canada, particularly those who study hydroelectric development in the postwar period, have found it especially useful because it links social and environmental change to political power, putting nature at the core of the project of modernization. This article reviews some of that literature to illustrate and critique Scott's key concepts and arguments. Specifically, it takes issue with Scott's contention that high modernist development pitted local knowledge against the expertise of the state's agents. The article also challenges the declensionist narrative offered in the book, which portrays the nature of high modernist change solely in terms of degradation. Finally, it suggests that adopting the very way of seeing that Scott criticizes – an imperial, encompassing, reductive vision – has, and can hold out, the promise for a better kind of development.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.113
Threshold uncertainty score0.819

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.012
Science and technology studies0.0090.013
Scholarly communication0.0080.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.190
Teacher spread0.175 · 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 designTheoretical or conceptual
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

Citations6
Published2016
Admission routes2
Has abstractyes

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