Reflections from interdisciplinary research on the social implications of implementing 30x30: five ways forward
Bibliographic record
Abstract
Area-based conservation is critical for conserving biodiversity, but its success depends on understanding and addressing its social dimensions. Here we share key reflections from an interdisciplinary working group studying the social implications of expanding area-based conservation under the Kunming-Montreal Global Biodiversity Framework’s Target 3, also known as 30×30. Over two years, our interdisciplinary working group collaborated through workshops, quantitative spatial analysis and qualitative case studies to explore how approaches to implementing Target 3 may create challenges and opportunities for people living in and around protected and conserved areas, particularly since international and even national priorities can sometimes conflict with local aspirations. Our reflections emphasise that implementing Target 3 is not only an ecological challenge but also a profoundly social one. Based on insights from our collective work, we identify five ways forward for a socially just Target 3: (1) fostering dialogue across perspectives to support more inclusive solutions; (2) giving greater attention to who is affected; (3) balancing the focus on ‘where’ conservation is implemented with more attention to ‘how’ it is governed and managed; (4) mainstreaming social data in conservation planning; and (5) connecting insights across scales. By sharing these reflections, we aim to support ongoing efforts to foreground social considerations in conservation policy and practice.
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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 | 0.090 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.063 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.014 | 0.038 |
| Insufficient payload (model declined to judge) | 0.005 | 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".