Effects on perceptions of greenspace benefits during the COVID-19 pandemic
Bibliographic record
Abstract
Calls for investment in green infrastructures, which can provide a range of ecosystem services in support of sustainability and resilience, are increasing amidst the climate crisis.The COVID-19 pandemic has revealed for many the important benefits of greenspace to cultural ecosystem services, particularly to individuals' own assessments of their mental and emotional health, or subjective well-being (SWB).This pandemic has also revealed the unevenness of these benefits.In order to better understand the contributions of greenspace to SWB, as well as the distribution of the benefits, during times of shared social-ecological disruption, we investigate perceptions of greenspace and their effect on SWB during the COVID-19 pandemic.We use a mixed methods approach combining data from surveys and interviews conducted with US post-secondary students.Our results indicate that perceiving the outdoors as good for you is related to higher levels of SWB.We also find that both prior experience with nature and current socialenvironmental circumstances play an important role in shaping this perception.When considered alongside research regarding environmental justice and children's access to nature, these findings suggest a need for both distributional and intergenerational justice in greenspace planning, design, and management, as well as explicit attention to the role of greenspace in coping with future socialecological disturbance.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".