Climate Change Leadership: Team Building, Change Agents, Planning, Strategy
Bibliographic record
Abstract
The climate emergency requires bold and wise leadership. The necessary response to climate change by humans is both simple and complex. It is simple insofar as the primary task is greenhouse gas emission reductions and equitably distributed preparations for the planetary heating that is already “baked in” to Earth systems. It is complex in that meaningful response to climate change requires comprehensive action across sectors to achieve the energy and social transition urgently required today. Government leaders and policy are lagging behind the urgency of the crisis. Citizens understand they must push for the required change. Many feel the time-honored tactic of nonviolent direct action is needed now to change policy. This chapter examines leadership needs and attributes during the climate emergency using three case studies: 1) the Wet&s;suwet’en Indigenous led efforts to stop fossil fuel pipelines on their territory; 2) the Canadian national Blue Dot campaign of municipalities and higher-level governments adopting a right to a healthy planet; and 3) British Columbian citizens efforts to block construction of a new diluted bitumen pipeline from the Alberta oil sands. Leadership in these efforts is found in people from all ages and walks of life. Iterative achievement of goals during a long campaign enables leadership to emerge from active groups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".