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Record W3186767566 · doi:10.1139/er-2021-0040

On embracing the concept of becoming environmental problem solvers: the trainee perspective on key elements of success, essential skills, and mindset

2021· article· en· W3186767566 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueEnvironmental Reviews · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInnovation, Sustainability, Human-Machine Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsMindsetEngineering ethicsEnthusiasmCurriculumRubricPsychologyComputer sciencePedagogyEngineeringSocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.303
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it