Transitioning to a green economy: Radical labor transformation or building upon existing skills?
Bibliographic record
Abstract
Transitioning to a “green” economy will require many industries to change their activities, raising concerns about the elimination of occupations and the need for significant retraining of the workforce. These concerns have increased resistance to a green transition from some sectors of society. Yet if skills embodied in current economic tasks can be reapplied to activities that facilitate a green transition, the retraining challenge might be lessened. Using a new taxonomy of sustainable economic activities – those that can contribute to climate change mitigation or adaptation – we estimate the number of US, German, and Canadian workers already employed in industries that are equipped to undertake sustainable economic activities. While the fraction of potential green workers varies considerably across metropolitan areas, in each country over one third of workers could conceivably contribute to a green economic transition by applying their existing skills to new activities. This represents more than 47 million workers in the US. Thus, a transition to a green economy may require more that firms reconfigure their workforces than individual workers reconfigure their skill sets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".