Essential skills for the training of conservation social scientists
Bibliographic record
Abstract
Since 2000, the field of biodiversity conservation has been reckoning with the historical lack of effective engagement with the social sciences in parallel with rapid declines in biodiversity and escalating concerns regarding socioecological justice exacerbated by many common conservation practices. As a result, there is now wide recognition among scholars and practitioners of the importance of understanding and engaging human dimensions in conservation practice. Developing and applying theoretical and practical knowledge related to the social sciences, therefore, should be a priority for people working in biodiversity conservation. We considered the training needs for the next generation of conservation social science professionals by surveying conservation professionals working in multiple sectors. Based on 119 responses, the 3 most cited soft skills (i.e., nontechnical abilities that facilitate effective interpersonal interaction, collaboration, and adaptability in diverse contexts) were cultural awareness and the ability to understand the values and perspectives of others, people management and conflict resolution skills, and the ability to develop and maintain inter- and intraorganizational networks and working relationships. The 3 most cited technical skills were expertise in behavior change expertise, expertise in government and policy, and general critical thinking and problem-solving skills. Overall, we found that current conservation social scientists believe students and early career conservationists should prioritize soft skills rather than technical skills to be effective. These skills were also correlated with the skills considered hardest to acquire through on-the-job training. We suggest early career conservationists develop essential soft and technical skills, including cultural awareness, networking, critical thinking, and statistical analysis tailored to sectoral and regional needs.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".