Navigating eco-anxiety and eco-detachment: educators’ strategies for raising environmental awareness given students’ disconnection from nature
Bibliographic record
Abstract
Awareness of environmental problems such as climate change can motivate action, but educators debate whether to raise students' awareness given that it may provoke eco-anxiety.We have even less understanding of how these relationships are affected by young people's growing disconnection from nature.Through 28 semi-structured interviews in Canada and the United Kingdom, we explore how educators perceive students' nature connection and eco-anxiety and how they introduce discussion of environmental problems.Educators frequently observed experiential, cognitive, and emotional indicators of nature disconnection and eco-anxiety, although many (39%) reported rarely, if ever, witnessing such environmentally related distress.Educators prioritised improving nature connection over raising awareness of environmental problems.When they discuss these issues with students, they emphasise hope and encourage pro-environmental behaviours to avoid eliciting eco-anxiety for those not currently experiencing it, a strategy that is partially inconsistent with literature suggesting some eco-anxiety can nurture pro-environmental behaviour.Our findings provide new insights into the challenges that educators face in helping their students navigate current environmental trends.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".