Pathways towards a sustainable future envisioned by early‐career conservation researchers
Bibliographic record
Abstract
Abstract Scientists have warned decision‐makers about the severe consequences of the global environmental crisis since the 1970s. Yet ecological degradation continues and little has been done to address climate change. We investigated early‐career conservation researchers' (ECR) perspectives on, and prioritization of, actions furthering sustainability. We conducted a survey ( n = 67) and an interactive workshop ( n = 35) for ECR attendees of the 5th European Congress of Conservation Biology (2018). Building on these data and discussions, we identified ongoing and forthcoming advances in conservation science. These include increased transdisciplinarity, science communication, advocacy in conservation, and adoption of a transformation‐oriented social–ecological systems approach to research. The respondents and participants had diverse perspectives on how to achieve sustainability. Reformist actions were emphasized as paving the way for more radical changes in the economic system and societal values linked to the environment and inequality. Our findings suggest that achieving sustainability requires a strategy that (1) incorporates the multiplicity of people's views, (2) places a greater value on nature, and (3) encourages systemic transformation across political, social, educational, and economic realms on multiple levels. We introduce a framework for ECRs to inspire their research and practice within conservation science to achieve real change in protecting biological diversity.
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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 | 0.037 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| 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".