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Saving Niagara From Itself: The Campaign to Preserve and Enhance the American Falls, 1965-1975

2018· article· en· W2804513163 on OpenAlexaboutno aff
Daniel Macfarlane

Bibliographic record

VenueEnvironment and History · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsWaterfallCommissionContext (archaeology)Government (linguistics)TourismPolitical scienceBusinessHistoryLawArchaeology

Abstract

fetched live from OpenAlex

Abstract Between 1965 and 1975, the United States and Canada investigated whether they should preserve and enhance the American Falls, one of the two main cataracts at Niagara Falls, by physically reengineering it. This campaign had its roots in local concerns, but tapped into wider sentiments and emotions about the famous waterfall, and came to involve multiple levels of government and the International Joint Commission. Engineers looked at whether it was feasible to remove all the rock at the base of the American Falls - the talus - which led to the dewatering of the waterfall in 1969. Using a range of techniques, including public consultations, the transborder experts concluded that it was feasible to give the waterfall a facelift, and presented a range of engineered options. However, the International Joint Commission ultimately recommended that it would be best to refrain from an interventionist approach and mostly leave the American Falls alone. Employing envirotech and emotional history approaches, this paper argues that over the course of a decade the meaning of 'preservation' in the context of Niagara Falls significantly shifted because of several factors: the emerging environmental movement, the cost of removing the talus and other alterations, evidence that the public wouldn't sufficiently appreciate these changes, and worries about tourism impacts.

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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.437
Threshold uncertainty score0.868

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.005
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0020.003
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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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