Conducting urban ecology research on private property: advice for new urban ecologists
Bibliographic record
Abstract
Private property makes up a large proportion of urban green space and differs from public green space in ecologically important ways. While including private property in urban landscape research is necessary, ecologists are frequently unprepared to work on private property and thus often exclude private land from empirical studies. To address this gap and encourage research on private property, we ask: ‘What lessons have urban ecologists learned from designing their research and completing their fieldwork that are relevant to researchers new to private property?’ We present 10 common methodological and practical challenges faced by urban ecologists, with solutions synthesized from semistructured interviews with 24 urban ecologists from 7 countries, along with public health researchers and police officers. The compiled advice addresses all stages of research, including research design, sample design, gaining access to study sites, collecting data on study sites and sharing results. Ecologists reported that their research and sampling design were shaped by the need to work with property owners, found communicating honestly and respectfully with property owners for the duration of the research influenced success, and emphasized practicing good field safety and preparing for both routine and stressful in-person encounters. Further research and collaboration among ecologists and private property owners is necessary to improve our understanding and management of urban ecosystems given the proportion of urban green space that is on private property. We hope that our suggestions will help guide the next generation of urban ecologists to take up this challenge.
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 | 0.026 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".