Rethinking scenario building for sustainable futures: mobilizing conscientização, social learning and knowledge co-production
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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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 | 0.044 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.003 | 0.036 |
| 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".