Bibliographic record
Abstract
Resumo Trata-se de discutir se a urbanização é passível de ser compreendida como parte do processo geral de estruturação da sociedade e do território. Um processo onde as desigualdades sociais e espaciais conjugadas à mobilidade espacial e setorial do trabalho contribuem para alterar o território. Este processo que cria fixos e fluxos tem uma resultante espacial em duas escalas: a cidade, na escala dos lugares; e a rede urbana, enquanto a manifestação espacial da cooperação entre lugares, na escala territorial. Isto não significa dizer que a urbanização em si é um determinante maior ou menor, mas um produto de práticas socais que interage com outros fatores na construção do espaço social além das cidades. Palavras-chave: urbanização, reprodução espacial, reestruturação. Abstract Our goal is to argue whether the urbanisation could be understood as a part of society and territory general structuring process. A process where the social and spatial inequalities articulated to spatial and sectorial mobility of labor contribute to transform the territory. This process that creates fix and flows has an spatial outcome in two scales: the city at the place scale and the urban network, as a spatial manifestation of the cooperation among places, at the territorial scale. Not meaning that urbanization by itself is a major or minor determinant, but a social practices product, which interacts with other factors on the construction of social space beyond the cities. Keywords: urbanization, space reproduction, restructuring.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".