Saving Niagara From Itself: The Campaign to Preserve and Enhance the American Falls, 1965-1975
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".