Re-Framing Data Narratives for Forest and Climate Futures: A Critical, Collaborative Approach to Data Activism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it