Four recommendations to tackle the complex reality of transdisciplinary, natural experiment research
Bibliographic record
Abstract
Natural experiments are often used to study interventions in which randomization to control versus intervention conditions are impossible. Nature-based interventions (i.e., programs designed to increase human interaction with nature and improve human health) are commonly studied as natural experiments. We used a natural experiment design to explore the benefits of ecological rehabilitation of parks on biodiversity and resident health in low-income, minoritized neighborhoods in Detroit, MI. Given the complexities and interconnectedness of lived experiences, community needs, and ecological health, this research design has presented challenges. Based on our experiences, we pose four key recommendations for researchers and practitioners conducting natural experiments, nature-based interventions, and those working in low-income, minoritized neighborhoods. We use the explicit examples of challenges faced as rationale for these recommendations. The key recommendations are (1) Engage with community leaders; (2) Build a transdisciplinary team and work closely; (3) Examine privilege; and (4) Create a unified vision.
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 | 0.553 | 0.730 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.021 | 0.014 |
| Research integrity | 0.068 | 0.054 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".