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Strong Winds and Widow Makers

2022· book· en· W4391085176 on OpenAlexaboutno aff
Steven C. Beda

Bibliographic record

VenueUniversity of Illinois Press eBooks · 2022
Typebook
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsStewardship (theology)LoggingWildlifeGeographyCommodityWork (physics)RecreationPolitical scienceBusinessEcologyPoliticsForestryEngineering

Abstract

fetched live from OpenAlex

Strong Winds and Widow Makers examines what the forests have meant to the rural working-class communities spread throughout timber country, the band of forestland stretching from southern Oregon to northern British Columbia. Countering pervasive public images that often portray the Northwest’s timber workers and the rural working class as indifferent to the ecological and environmental effects of logging, this book shows that an ethic of forest stewardship has long been central to the culture and history of the people who’ve lived and worked in the Northwest woods. A desire to simultaneously protect their communities and the forests where those communities were located has shaped the identity and culture of timber workers and driven labor activism in the region, from the industrial unions of the twentieth century to the fights to protect jobs and the timber industry in the later twentieth century. Understanding this history provides new ways of thinking about people who work in nature and about the forests themselves. Listening to the voices of loggers and people from timber-working communities reminds us that forests, for all their beauty and grandeur, provide an important commodity that remains central to US and Canadian national and global economies. Listening to the voices of timber workers helps thinking about ways to balance economic uses of the forest with protecting wildlife habitat, biodiversity, and recreational space.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.096
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.159
Teacher spread0.149 · 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 teacher head, not a consensus.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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