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Regulating America’s Natural Environment

2018· reference-entry· en· W2791430722 on OpenAlexaboutno aff
Bart Elmore

Bibliographic record

VenueOxford Research Encyclopedia of American History · 2018
Typereference-entry
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental movementGovernment (linguistics)Public administrationNatural resourceWildernessAgency (philosophy)BureaucracyEnvironmental stewardshipPolitical scienceEnvironmental historyEnvironmental protectionPoliticsGeographyEnvironmental resource managementEcologyLawSociologyEconomic historyEconomics

Abstract

fetched live from OpenAlex

Abstract From the founding of the American republic through the 19th century, the nation’s environmental policy mostly centered on promoting American settlers’ conquest of the frontier. Early federal interventions, whether railroad and canal subsidies or land grant acts, led to rapid transformations of the natural environment that inspired a conservation movement by the end of the 19th century. Led by activists and policymakers, this movement sought to protect America’s resources now jeopardized by expansive industrial infrastructure. During the Gilded Age, the federal government established the world’s first national parks, and in the Progressive Era, politicians such as President Theodore Roosevelt called for the federal government to play a central role in ensuring the efficient utilization of the nation’s ecological bounty. By the early 1900s, conservationists established new government agencies, such as the U.S. Forest Service and the Bureau of Reclamation, to regulate the consumption of trees, water, and other valuable natural assets. Wise-use was the watchword of the day, with environmental managers in DC’s bureaucracy focused mainly on protecting the economic value latent in America’s ecosystems. However, other groups, such as the Wilderness Society, proved successful at redirecting policy prescriptions toward preserving beautiful and wild spaces, not just conserving resources central to capitalist enterprise. In the 1960s and 1970s, suburban and urban environmental activists attracted federal regulators’ attention to contaminated soil and water under their feet. The era of ecology had arrived, and the federal government now had broad powers through the Environmental Protection Agency (EPA) to manage ecosystems that stretched across the continent. But from the 1980s to the 2010s, the federal government’s authority to regulate the environment waxed and waned as economic crises, often exacerbated by oil shortages, brought environmental agencies under fire. The Rooseveltian logic of the Progressive Era, which said that America’s economic growth depended on federal oversight of the environment, came under assault from neoliberal disciples of Ronald Reagan, who argued that environmental regulations were in fact the root cause of economic stagnation in America, not a powerful prescription against it. What the country needed, according to the reformers of the New Right, was unregulated expansion into new frontiers. By the 2010s, the contours of these new frontiers were clear: deep-water oil drilling, Bakken shale exploration, and tar-sand excavation in Alberta, Canada. In many ways, the frontier conquest doctrine of colonial Americans found new life in deregulatory U.S. environmental policy pitched by conservatives in the wake of the Reagan Revolution. Never wholly dominant, this ethos carried on into the era of Donald Trump’s presidency.

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)0.002
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: Review · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.004
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0130.001

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 head0.025
GPT teacher head0.268
Teacher spread0.242 · 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
GenreReview

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
Published2018
Admission routes1
Has abstractyes

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