On embracing the concept of becoming environmental problem solvers: the trainee perspective on key elements of success, essential skills, and mindset
Bibliographic record
Abstract
Life in the Anthropocene is characterized by many environmental problems, and unfortunately, more continue to emerge. Although much effort is focused on identifying problems, this does not necessarily translate to solutions. This situation extends to the training environment, where students are often adept at understanding and dissecting problems but are rarely explicitly equipped with the skills and mindset to solve them. Herein, a group of undergraduate students and their instructors consider the concept of becoming environmental problem solvers. We first identified themes associated with historical and contemporary environmental successes that emerged from our reading, or more specifically, we identify the elements that underlie environmental success stories. The key elements of success involved setting clear objectives, identifying the scale of the problem, learning from failure, and consulting diverse knowledge sources. Next, we reflected on the skills and mindset that would best serve environmental problem solvers and enable future successes. Essential skills include innovative and critical thinking, ability to engage in collaborative teamwork, capacity to work across boundaries, and resilience. In terms of mindset, key attributes include the need for courage, enthusiasm and commitment, optimism, open mindedness, tenacity, and adaptability. We conclude with a brief discussion of ideas for revising training and curriculum to ensure that students are equipped with the aforementioned skills and mindset. The ideas shared here should contribute to ensuring that the next generation of learners have the ability to develop solutions that will work for the benefit of the environment, biodiversity, and humanity. Solving environmental problems will increasingly fall to the next generation, so it is time to ensure that they are prepared for that task.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".