Scaling just transitions: Legitimation strategies in coal phase-out commissions in Canada and Germany
Bibliographic record
Abstract
Climate change mitigation triggers both spatial and moral complexities, as demonstrated by the contentious issue of phasing out coal power. The success of the Paris Agreement depends on, among other things, the acceptability of climate policy measures and thus, from a moral perspective, on the ability to organize transition processes in ways that do not damage the livelihoods of workers, communities, and entire regions. Spatially, the unequal distributions of burdens and advantages of both climate change and respective mitigation measures provoke struggles over their legitimacy in contexts ranging from local to global. Phasing out coal mining and the respective power generation capacity thus triggers processes of structural transformation that cut across geographic scales, vertical levels of policy and politics, as well as sectoral boundaries. In light of the urgency of the climate crisis, countries such as Canada and Germany have established stakeholder-driven commissions to develop proposals for just transition pathways for phasing out coal production and consumption. We argue that these commissions are arenas in which spatial, moral, and sectoral (re-)negotiations materialize. Comparing the Canadian and German stakeholder commissions through expert interviews with their members, the article traces how governments use commissions to legitimize their transition policies. Expectations at different levels and from different actors in turn place commission members under pressure to justify their involvement and the outputs of the commissions. We find that the Canadian task force showed greater commitment to collecting and reflecting the needs of communities in its coal regions, and to communicating these to the federal government. In the German coal commission, legitimation strategies focused mainly on a broad representation of interests, and on government spending for affected regions, workers, and industries. In that case, a compromise was reached that satisfied most, but not all, of the diverse requirements.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.031 | 0.016 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".