Research trends in U.S. national parks, the world's “living laboratories”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract U.S. national parks are essential public assets for preserving natural and cultural resources and for decades have provided natural laboratories for scholarly research. However, park research, and how it may be biased, has not been inventoried at a national scale. Such a synthesis is crucial for assessing research needs and planning for the future. Here, we present the first comprehensive summary of national park research using nearly 7,000 peer‐reviewed research articles published since 1970. We report when and where these studies occurred, what academic disciplines were most represented, and who funded the research. Our findings show that publication rates increased rapidly during the 1990s and 2000s, but since about 2013 have declined. Over half of the studies occurred in five parks, with Yellowstone representing over a third of all studies, followed by Everglades, Great Smoky Mountains, Glacier, and Yosemite. Nearly half of the studies occurred in the Northwestern Forested Mountains ecoregion. The life sciences, particularly ecological studies, contributed the majority of park research, although the earth sciences dominated several arid ecoregions of the West. Federal agencies funded the largest proportion of research, followed by U.S. universities, non‐profit organizations, federal programs (mainly the National Science Foundation), state agencies, and private industry. Over a quarter of the research was supported by international sources. Recent declines in scholarly output suggest that national park research directions and funding opportunities should be examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 it