Finding shared meaning in the Anthropocene: engaging diverse perspectives on climate change
Bibliographic record
Abstract
Abstract The scientific evidence of climate change has never been clearer and more convergent, and calls for transformations to sustainability have never been greater. Yet, perspectives and social opinions about it remain fractured, and collaborative action is faltering. Climate policy seeks to forge a singular sense of climate change, dominated by an ‘information deficit model’ that focuses on transferring climate science to the lay public. Critics argue that this leaves out certain perspectives, including the plurality of meanings uncovered through participatory approaches. However, questions remain about how these approaches can better account for nuances in the psychological complexity of climate change, without getting stuck in the cul-de-sacs of epistemological relativism and post-truth politics. In this paper, I explore an approach through which we might find shared meaning at the interface of individual and collective views about climate change. I first present a conceptual framework that describes five psychological reasons why climate change challenges individual and collective meaning-making, and also provides a way to understand how meaning is organized within that. I then use this framework to inform the use of photo voice as a transformative (action-research) method, examining its ability to overcome some of the meaning-making challenges specific to climate change. I discuss how participants from a coffee cooperative in Guatemala reflected first on their own climate meanings and then engaged in a meaning-making process with other actors in the coffee value chain. Findings suggest a psychosocial approach to climate engagement—one that engages both subjectively and intersubjectively on the complexities unique to climate change—is helpful in acknowledging an ontological pluralism of ‘climate changes’ amongst individuals, while also supporting a nexus-agreement collectively. This may in turn contribute to a more effective and ethical process of transformation.
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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 | 0.032 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.049 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".