The seven lamps of planning for biodiversity in the city
Bibliographic record
Abstract
Cities tend to be built in areas of high biodiversity, and the accelerating pace of urbanization threatens the persistence of many species and ecological communities globally. However, urban environments also offer unique prospects for biological conservation, with multiple benefits for humans and other species. We present seven ecological principles to conserve and increase the biodiversity of cities, using metaphors to bridge the gap between the languages of built-environment and conservation professionals. We draw upon John Ruskin's famous essay on the seven lamps of architecture, but more generally on the thinking of built-environment pioneers such as Patrick Geddes (1854–1932) who proposed a synoptic view of the urban environment that included humans and non-humans alike. To explain each principle or ‘lamp’ of urban biodiversity, we use an understanding from the built-environment disciplines as a base and demonstrate through metaphor that planning for the more-than-human does not require a conceptual leap. We conclude our discussion with ten practical strategies for turning on these lamps in cities. Urban planners, architects, landscape architects, engineers and other built-environmental professionals have a key role to play in a paradigm shift to plan for the more-than-human, because of their direct influence on the evolving urban environment. This essay is intended to increase dialogue between ecologists and members of these professions, and thus increase the biodiversity of cities around the world.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".