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Record W2051866602 · doi:10.1080/13504622.2015.1018141

Environmental education in a neoliberal climate

2015· article· en· W2051866602 on OpenAlexaff
David Hursh, Joseph A. Henderson, David Greenwood

Bibliographic record

VenueEnvironmental Education Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsLakehead University
Fundersnot available
KeywordsNeoliberalism (international relations)The ImaginarySociologyIndividualismPoliticsCONTESTPolitical economyPrivilege (computing)EnvironmentalismPolitical scienceEnvironmental ethicsLaw

Abstract

fetched live from OpenAlex

This introduction to a special issue of Environmental Education Research explores how environmental education is shaped by the political, cultural, and economic logic of neoliberalism. Neoliberalism, we suggest, has become the dominant social imaginary, making particular ways of thinking and acting possible while simultaneously discouraging the possibility and pursuit of others. Consequently, neoliberal ideals promoting economic growth and using markets to solve environmental and economic problems constrain how we conceptualize and implement environmental education. However, while neoliberalism is a dominant social imaginary, there is not one form of neoliberalism, but patterns of neoliberalization that differ by place and time. In addition, while neoliberal policies and discourses are often portrayed as inevitable, the collection shows how these exist as an outcome of ongoing political projects in which particular neoliberalized social and economic structures are put in place. Together, the editorial and contributions to the special issue problematize and contest neoliberalism and neoliberalization, while also promoting alternative social imaginaries that privilege the environment and community over neoliberal conceptions of economic growth and hyper-individualism.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.008

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 head0.363
Teacher spread0.338 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations187
Published2015
Admission routes1
Has abstractyes

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