Designing agile pathways for climate adaptation skill development
Bibliographic record
Abstract
Capacity building for advancing climate-change leadership has become a critical workforce development requirement for both professionals and front-line workers. As the World Economic Forum Jobs 2020 report noted, there is an increasing need to provide short-timeframe opportunities for reskilling and upskilling that will keep step with the increasing issues of the climate crisis. Micro-credentials have been proposed as a strategy to enable the ongoing development of knowledge and skills to address this workforce development requirement, which we examine in the context of a university initiative that has prototyped skill pathways to address key climate adaptation themes.We report and discuss the strategic use of the Climate Adaptation Competency Framework (2020)–a Creative Commons-licensed (CC) open competency framework–along with the use of open educational resources to create agile pathways to skill development for climate adaptation and action. The pathways we have designed and are testing combine self-directed learning resources, individual and group activities, and authentic assessment practices to validate skill development. Micro-credentials are awarded from a university continuing and professional studies division to learners from multiple practice domains for demonstrations of competence.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".