Exploring Eco-Grief, Transformative Learning, and Action in Environmental Observers
Bibliographic record
Abstract
This research aimed to contribute to understanding emotional reactions to ecological change in “environmental observers,” who purposely observe the environment and environmental information as part of their work or role in society (e.g., citizen scientists, environmental professionals, Indigenous knowledge keepers). People in such roles are vulnerable to experiencing negative emotions, which could, in turn, affect their decision to keep engaging in their work and (or) other pro-environmental behaviours. We used the term “eco-grief” to discuss such emotions and applied a phenomenological approach to understand how environmental observers’ learning adjacent to ecological loss impacted their emotions, decisions, and actions. We worked with Mezirow’s transformative learning as a theoretical framework, which characterizes learning through critical self-reflection and re-evaluations in perspective and connects it to decision-making and action (i.e., transformation). We categorized such learning within Mezirow’s instrumental and communicative domains and attached them to the different forms of action reported by the observers. Finally, we considered how engaging in action potentially transforms emotions. Instrumental and communicative domains proved to relate to different emotional responses and forms of action, providing insight for developing programs and support for observers.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".