Bibliographic record
Abstract
Many peer-reviewed research publications have concluded that “experience of nature” is beneficial for mental health and well-being, but virtually all of them offer only fuzzy definitions of “nature,” or none at all, and the “nature” to which subjects are exposed is itself fuzzy. This commentary argues that accounting for the two kinds of fuzziness are the underappreciated roles of artifacts and natural kinds (as understood by cognitive psychologists and philosophers of science) in both researcher and subject thinking which involves quasi-natural places and scenes. Artifacts, if discerned, adulterate what might otherwise be considered “nature.” They arouse thinking about the intentions behind them and in doing so they may trigger rumination. Rumination is associated with depression and other undesirable mental states, now rampant in urban populations. Instances of natural kinds, by definition and in contrast, generally do not express human intentions, so attending to them entails less rumination. The commentary suggests several potential explanations for why exposure to fuzzy “nature” may be healthful despite the fact that a “green” landscape or scene abounds in artifacts. It ends with some implications for research and park practice.
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.
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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.059 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 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".