SUPPLEMENT TO THE THIRD QUARTERLY REPORT ON THE ECONOMIC IMPACT OF THE AMERICAN RECOVERY AND REINVESTMENT ACT OF 2009 THE ARRA AND THE CLEAN ENERGY TRANSFORMATION
Bibliographic record
Abstract
A central piece of the American Recovery and Reinvestment Act of 2009 (ARRA) is more than $90 billion in government investment and tax incentives to lay the foundation for the clean energy economy of the future. As discussed in CEA’s Second Quarterly Report on the impact of the ARRA, this investment will help create a new generation of jobs, reduce dependence on oil, enhance national security, and improve the environment. 1 Ultimately, the investments could help transform the United States into a global clean energy leader. The ARRA clean energy investments are also providing crucial stimulus to the economy. Through programs such as enhanced tax credits for homeowners who make energy-efficient improvements, funding for research into new clean energy technologies, or grants to qualifying businesses, these investments are generating economic activity and creating new employment opportunities. This supplement to the CEA’s Third Quarterly Report updates our estimates of the effect of the ARRA’s clean energy provisions on economic recovery through the first quarter of 2010. We find that the Recovery Act directly created more than 80,000 clean energy jobs in the first quarter of 2010, and that the clean energy investments supported an additional 20,000
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.029 |
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".