Forest Urbanisms : New Non-Human and Human Ecologies for the 21st Century (Edition 1)
Bibliographic record
Abstract
How forest urbanism can address the contemporary socio-ecological crisis A radical redefinition of how humanity occupies the earth — through forests, agriculture, and settlement — and rearticulates environmental stewardship by intertwining ecologies and urbanisms, this publication brings together essays by scholars in forestry, urbanism and other disciplines, designers, practitioners and policy makers. It explores the multifaceted notion of forest urbanisms, including a conceptual framing essay; contributions from the sciences such as bioscience engineering, architecture, urbanism and public policy; contemporary forest urbanism projects and explorative essays that make tangible an agenda for the 21st century. With descriptions of both built and non-built projects from around the globe, the essays show how such projects substantiate a radical shift in humankind’s occupation of the world, where ecologies and urbanisms converge and agriculture, forests, and settlements are integrated. Forest Urbanisms extends growing research on a new nature–culture relationship, the necessity for trees in cities, and a rebalancing of ecology and urbanism. Contributing authors: Chiara Cavalieri (UC Louvain), Cecil Koninendijk (Nature Based Solutions Institute), Rik De Vreese (European Forest Institute), Bart Muys (KU Leuven), Colleen Murphy-Dunning (Yale University), Bureau Bas Smets, Kongjian Yu (Turenscape), Wim Wambecq and Joris Moonen (MIDI), Embyá Paisagens & Ecossistemas, EFFEKT, TCL, aldayjover architecture and landscape, Björn Bracke (KU Leuven / Kollektif Landscape), Koenraad Danneels (KU Leuven), Marlène Boura (Biotope Environnement, Région de Bruxelles-Capitale), Swagata Das (KU Leuven), Kamni Gill (University of Manitoba), Alejandra Parra-Ortiz (University of Montreal), Gina Serrano-Aragundi (EPA Barranquilla Verde), Jörg Rekittke (University College Dublin), Takako Tajima (University of Southern California), Jamie Vanucchi (Cornell University), Maria Goula (Cornell University) Ebook available in Open Access.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".