The perspective of youth: envisioning transformative pathways and desirable futures for people and nature
Bibliographic record
Abstract
Abstract This paper examines the pathways to desirable nature futures as envisioned by 22 young people from all United Nations regions and diverse cultural backgrounds who participated in the second edition of the IPBES Youth workshop (2022). The workshop employed the Three Horizons framework and the Nature Futures Framework (NFF) to describe the plurality of youth visions for desirable nature futures and transformative pathways to achieve these visions. Based on the outcomes of the workshop, we conducted a qualitative content analysis categorizing the ideas and quantitatively assessed commonalities and differences among workshop groups, which were based on the NFF perspectives (nature for nature, nature for society, nature as culture, and a group in between perspectives). There were important differences in the visions and pathways articulated by the groups, but also commonalities, such as the importance of governance, community-based approaches, and education for achieving desirable nature futures. We also discuss the importance of flexibility in the NFF to accommodate diverse perspectives and involvement of youth in shaping global sustainability agendas. While many ideas raised by young people during this workshop align with existing conservation narratives, the study reveals the need to foster new and innovative ideas to drive transformative change that is sensitive to diverse contexts, histories, and experiences.
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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 | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".