Towards a Linking Activist Pedagogy: Teacher Activism for Social-Ecological Justice
Bibliographic record
Abstract
The world is currently in the midst of a social-ecological crisis. We cannot ignore that the primary cause of this change in our planet's ecological balance and the increase in social injustices is our heavy dependence on non-renewable fossil fuels and a capitalist economic system, which encourages exploitation of both human and more than human resources with no regard for the consequences. In such a reality, it is alarming that education treats knowledge as disconnected fragments and that environmental and social issues are often addressed separately in education. In order to live in environmentally healthy and socially just communities, we need ways of thinking and teaching that integrate rather than fragment issues. There is a need to recognize that the ecological crisis is a “cultural crisis”. With the need for such an approach in mind, Morgan Gardner (2005) formulated the term “linking activism” to describe one's “blended social-ecological justice practice” when “being positioned in a single construct” (p.3). I extend this into a consideration of environmental and social-justice educators as agents of change whose daily activism works to change the current cultural paradigm and bring social-ecological order and harmony. This paper will argue for the importance of engaging in linking activism in education by critically examining the mainstream environmental educational field in order to critique its paradigm that is imprinted by the current dominant culture, which in turn perpetuates social-ecological oppression.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".