Addressing climate adaptation in education, research and practice: the CLiVIA-network
Bibliographic record
Abstract
Purpose Climate change and variability are both a developmental and an environmental issue. Adaptation to climate change and variability has gained a prominent place on global and local policy agendas, evolving from mainly climate risks impacts and vulnerability assessments to mainly adaptation action, imposing new defies to higher education (HE). The purpose of this paper is to introduce the Climate Vulnerability, Impact, and Adaptation (VIA) Network (CliVIA-Net), a South American university-based coalition aimed at achieving a science for/of adaptation. Design/methodology/approach CliVIA-Net is a collaborative effort by academic groups from across the spectrum of the natural, social and health sciences focused on improving climate VIA on education, research and practice. In consonance with international literature and practices, the network shifted from a discipline-oriented approach to an interdisciplinary and Earth System Science (ESS)-oriented one. It seeks to advance fundamental understanding and participatory practice-oriented research and to develop a problem orientation question/solving answering methodology. A set of cases studies illustrates how CliVIA-Net faces adaptation and sustainability challenges in the twenty-first century. Findings Focusing on interdisciplinary graduate education, practice-oriented research and problem orientation practice on climate threats which are already threatening the environment, population’s well-being and sustainability, allows for the co-production of knowledge and solutions, as well stakeholders’ buy-in and commitment. Originality/value CliVIA-Net draws upon the results of evolving interdisciplinary approaches on global change and VIA education, the research partnership with stakeholders and decision-makers to develop environmental and health outcomes, e.g. vulnerability indicators and scenario planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".