Bibliographic record
Abstract
Urban forestry, as the name implies, is a branch of forestry that deals with trees and woodlands in urban areas. Practice in urban forestry may have its foundations in the nineteenth century, but the moniker of urban forestry launched in earnest in the 1970s largely through the contributions of Eric Jorgensen, a professor at the University of Guelph on Ontario, Canada. Urban forestry combines long traditions of science and management in the fields of arboriculture (management of individual trees), silviculture (management of stands of trees), and forestry (management of larger woodlands comprised of many stands). Urban forestry, according to Jorgensen (see Konijnendijk, et al. 2006, cited under Definitions) is “a specialized branch of forestry and has as its objectives the cultivation and management of trees for their present and potential contribution to the physiological, sociological and economic well-being of urban society” (p. 95). This bibliography concentrates on the ecology of urban forests. Given that ecology is the study of organisms and their relationships with the biotic and abiotic environments, one instantly recognizes the fundamental ecological nature upon which urban-forest studies must rest. No trees in the urban ecosystem—whether in the heart of downtown or in the peri-urban outskirts of the town or city, or indeed anywhere between—escape the influence of humans, especially their built infrastructure. So urban forestry as a science and practice cannot help but rest firmly on the foundation of urban forest ecology. However, scoping this domain of science is fraught with pitfalls because the boundaries are unclear, broad, and porous. Much of forest ecology in general pertains to all forests, not just hinterland forests nor timber-producing forests. Equally complicating the scoping problem are the numerous intense relationships between people and trees which justify the notion that urban forests are best understood as social-ecological systems with vital economic and technical dimensions. Hearty thanks to K. E. Turner and C. Ordóñez Barona for assistance in identifying relevant literature, and to an anonymous reviewer for revisions suggestions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.082 | 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; 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".