Why Do Firms Adopt Advanced Environmental Practices (And Do They Make a Difference)?
Bibliographic record
Abstract
ational publications such as The New York Times, The Wall Street Journal and U.S. News and World Report. He also has been a featured commentator in PBS documentaries on global competitiveness and the future of work. ?? He has served as an advisor to the White House Office of Science and Technology Policy, the U.S. Department of Commerce, the U.S. Congress, state and local governments, the Canadian government, the European Union, the Japanese government, and multinational corporations. ?? Working with the Council of Great Lakes Governors --an organization of the governors of the U.S. Industrial Midwest, Florida helped design the successful economic development and environmental revitalization strategy for that region. ?? Florida earned his Bachelor's degree from Rutgers College, studied at MIT, ad received his Ph.D. from Columbia University in New York City. INTRODUCTION Since the dawn of the industrial age, the goals of economic growth and enhanced environmental quality have been a
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".