Academic Incentives Should Not Promote the “Extinction of Nature Experience”
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
Evidence suggests that a decline in people’s exposure to nature corresponds to decreasing support for nature—a phenomenon we call extinction of nature experience. Here, we evaluate three current trends in conservation research and consider if they contribute to a decrease in exposure to nature. We suggest that while using sensors, algorithms, technocentric thinking, conducting meta-analyses, and taking more lab-based approaches all have significant potential to advance conservation goals, they lead to researchers spending less time in the field and an extinction of nature experience. A reduction of researcher field time will mean fewer local field assistants are hired and trained; lower engagement of researchers with ground realities; and a rift in conservation research, planning, and implementation. We suggest that the field of conservation science should balance how it allocates time and rewards to field versus non-field components. If we are not careful, we will select researchers that are distant from the biodiversity itself and the communities that are affecting it locally. Since the pandemic began many researchers were unable to go to their field sites and if care is not taken, the pressures that promote the extinction of nature experience may be promoted by institutions in a post–COVID-19 world.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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