Bibliographic record
Abstract
The physical environment of cities and processes of urbanization have a long‐standing presence in the field of physical geography and can give physical geography a renewed relevance in urban sustainability and planning. Existing approaches in physical geography will be a valuable component of this work. New insights and understanding of urban environments may be gained by engaging with ideas of urban landscapes as socio‐natures; adopting critical, political, and reflexive modes of thought and practice; and thinking beyond the physical structures and spatial boundaries of the city to planetary urbanization. Thinking about urban environments in these ways also opens up possible changes in the scope of, and approaches to, physical geography as a whole. Urbaniser la géographie physique L'environnement physique des villes et les processus d'urbanisation sont des sujets bien établis dans le domaine de la géographie physique qui peuvent contribuer à assurer sa pertinence en regard du développement durable et de l'urbanisme. Les approches existantes en géographie physique constitueront un atout pour ce travail. De nouvelles connaissances sur les environnements urbains peuvent être obtenues en partant de l'idée que les paysages urbains sont une construction socio‐naturelle ; en adoptant une perspective critique, politique et réflexive des cadres de pensée et d'action ; et en posant un regard sur l'urbanisation planétaire au‐delà des structures physiques et des limites territoriales de la ville. Cette façon de concevoir les environnements urbains permet d'accroître la portée et de modifier les approches de la géographie physique dans son ensemble.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".