Re-Framing Data Narratives for Forest and Climate Futures: A Critical, Collaborative Approach to Data Activism
Bibliographic record
Abstract
As part of the project Forest Carbon Futures, I present reflections from a community-based initiative to co-design public resources for data understanding, engagement, and advocacy at the intersection of forest and climate research and policy. This work leverages critical, creative approaches, strategies and insights from visual communication design, narrative visualisation, and related practices to express complex forest carbon data in ways that preserve ecological specificity while supporting meaningful connections between diverse publics, data representations, more-than-human communities, and real-world implications and possibilities. Through a lens of storytelling and ecological situatedness, we seek to re-frame extractive narratives that homogenise and decontextualise the forest, and foster visual sense-making practices that convey a visceral sense of place alongside the complex, mycelial role of forest carbon in our lives. Here I discuss initial insights from our co-design process in order to inform future work surrounding ecological and climate data literacies, focusing particularly on avenues for invoking ecological place and narrative in fostering community-organising, policy-making, and advocacy.
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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 | 0.068 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.005 | 0.008 |
| 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".