Low-Carbon Transition as Vehicle of New Inequalities? Risk-Class, the Chinese Middle-Class and the Moral Economy of Misrecognition
Bibliographic record
Abstract
Low-carbon innovation is usually depicted as an exemplar of pursuit of the common good, in both mainstream policy discussion and the emerging orthodoxy of transition studies. Yet it may emerge as a key means of intensifying inequality. We analyse low-carbon innovation as a social and political process through the prism of differential risk-classes, focusing on the pivotal global case of emergence of the Chinese middle-class in seaboard megacities, especially regarding the profound challenges of urban e-mobility transition. This approach shows emergence of this still-forming sociopolitical grouping as tightly and complementarily coupled with the assembling of innovations that meaningfully tackle global risks, such as climate change, while also intensifying existing inequalities. Misrecognition of the duality of low-carbon innovations as both moral technologies and as relatively expensive consumer products has the potentiality to be a key mechanism of this process, thereby serving to reproduce, constitute and legitimize inequalities in novel and unexpected ways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".