Rethinking scenario building for sustainable futures: mobilizing conscientização, social learning and knowledge co-production
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
Scenario building is a powerful tool for evaluating drivers of environmental change and assessing alternative socioecological pathways, helping integrate science-based information into decision-making. Nonetheless, this potential has not been fully embraced by scientists and decision-makers, in part owing to limitations of current scenario frameworks at representing the diversity of values for nature and potential transformative changes to bend the biodiversity loss curve. There is still a need to further develop scientists’ capacities to include a transdisciplinary perspective in scenario building to address the drivers of transformative change. This paper addressesthese needs by reflecting on the role of scientists engaged in scenario building in the construction of sustainable futures through the lens of three key concepts: social learning, knowledge co-production and conscientização (a Portuguese term meaning to build sociopolitical awareness and take action). Drawing on a survey of participants of a Scenario Building School and a literature review, we suggest that scientists require capacity building to leverage these concepts together for the construction of transformative futures. This includes addressing power imbalances, improving inclusive and transdisciplinary participatory methods, reaching consensus and promoting action. We recommend that scientists engaged in scenario building focus on fostering transformative changes, challenging mainstream storylines, embracing diversity and addressing inequalities to pursue sustainable futures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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