Value archetypes in future scenarios: the role of scenario co-designers
Bibliographic record
Abstract
The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) relies on future scenarios in its assessments of global social-ecological systems. Scenarios explicitly or implicitly embed normative positions (e.g., values for nature, nature’s contributions to people, good quality of life). Such scenario values shape how scenario narratives evolve, e.g. through driving forces, framings, or ways how decisions are legitimized within a given scenario. Initial research in futures studies has examined how scenario values depend on whose voices are included in scenario co-design. However, less attention has been paid so far to explicitly assessing the extent to which scenario values are associated with different types of scenario co-designers. Our paper expands this knowledge with a set of novel analyses building on the comprehensive review of scenarios in the IPBES values assessment. To this end, we conducted a formal archetype analysis of 257 scenarios assessed in the IPBES values assessment to identify re-appearing archetypal configurations of values and their link to the actors involved as scenario co-designers. The results show that scenarios valuing nature for itself and its benefits to societal well-being were co-designed by experts and academics less frequently than expected under the assumption of stochastic independence; on the contrary, such scenarios were co-designed more frequently than expected by governmental and community actors. The paper illustrates how archetype analysis can contribute to the validation and further development of scientific knowledge feeding into science-policy assessments. The findings are important to acknowledge how scenarios express and possibly re-enforce peoples’ normative positions, and what role values might play when scenarios get translated into real-world decisions and actions.
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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 | 0.063 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".