Environmental education in a neoliberal climate
Bibliographic record
Abstract
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".