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Record W2912507396

Environmental Activism on the Ground: Small Green and Indigenous Organizing

2019· book· en· W2912507396 on OpenAlexaboutno aff
Jonathan Clapperton, Liza Piper

Bibliographic record

Venuenot available
Typebook
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEnvironmentalismGeographyEnvironmental ethicsPolitical scienceEcologyLawBiologyPoliticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Environmental Activism on the Ground draws upon a wide range of interdisciplinary scholarship to examine small scale, local environmental activism, paying particular attention to Indigenous experiences. It illuminates the questions that are central to the ongoing evolution of the environmental movement while reappraising the history and character of late twentieth and early twenty-first environmentalism in Canada, the United States, and beyond.This collection considers the different ways in which Indigenous and non-Indigenous activists have worked to achieve significant change. It examines attempts to resist exploitative and damaging resource developments, and the establishment of parks, heritage sites, and protected areas that recognize the indivisibility of cultural and natural resources. It pays special attention to the thriving environmentalism of the 1960s through the 1980s, an era which saw the rise of major organizations such as Greenpeace along with the flourishing of local and community-based environmental activism.Environmental Activism on the Ground emphasizes the effects of local and Indigenous activism, offering lessons and directions from the ground up. It demonstrates that the modern environmental movement has been as much a small-scale, ordinary activity as a large-scale, elite one.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.010
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
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 head0.041
GPT teacher head0.286
Teacher spread0.245 · 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 designQualitative
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

Citations7
Published2019
Admission routes1
Has abstractyes

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