The EJAtlas: An Unexpected Pedagogical Tool to Teach and Learn About Environmental Social Sciences
Bibliographic record
Abstract
Abstract This chapter examines how the Environmental Justice Atlas (EJAtlas), an online platform that was initially developed by ICTA-UAB—during the EJOLT international project—to make visible and systematize contemporary struggles against environmental injustice worldwide is becoming an attractive interactive tool to teach and learn about Environmental Social Sciences such as Political Ecology, Ecological Economics, Environmental Sociology, Human Geography, Critical Cartography; as well as Environmental Humanities, in Peace and Conflict studies. In this vein, the EJAtlas has unexpectedly become a tool for teaching at undergraduate and graduate levels that is already being used in diverse countries like Argentina, Bolivia, Canada, China, Mexico, Spain, Turkey, the UK, or the USA. This chapter examines why and how the EJAtlas is used for teaching/learning Environmental Social Science–related contents. We analyze the main challenges and lessons around what is taught, to whom, and why. We discuss how The EJatlas has the potential to not only raise awareness on environmental sustainability but also to address some key concerns regarding the demotivating ‘remoteness’ students might feel due to distance from on-the-ground issues and activism, and the lack of diverse voices present in course material (particularly voices from the frontlines of environmental injustices and resistance movements), along with the difficult balance to strike between theory and practice. The Atlas offers a platform that students and educators can use to help bridge these gaps- by providing a way for students to tangibly engage with important environmental resistance movements, visibilizing diverse, frontline voices and experiences, and connecting the theoretical to the practical via a range of opportunities for promoting environmental justice work outside of the classroom including advocacy, documentation, networking, and solidarity-building.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".