Temporal trends of environmental concern and potential influencing factors in the United States and China, 1945–2019
Bibliographic record
Abstract
Abstract Past investigations into the temporal trends of environmental concern (EC) yielded varying results across different time spans and nations, highlighting the need for nation‐specific studies with extended time frames. Using data from Google Ngram Viewer, this pre‐registered study examined the temporal trends and influencing factors of EC as reflected in books published in the United States and China—the world's two largest greenhouse gas emitters—between 1945 and 2019. The findings revealed distinct patterns between the two countries. In the United States, EC rose sharply in the 1960s, peaked in the 1990s, and declined steeply thereafter, with local environmental problems emerging as the most stable predictor of this trend. In contrast, in China, EC has steadily increased since the 1980s, potentially driven by both local and global environmental problems as well as post‐materialist values. These findings highlight that while EC trends and their potential determinants share commonalities across nations, they also exhibit unique characteristics. This underscores the importance of considering each society's distinct socio‐historical context when examining the evolution of EC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".