Extended urbanization in and from Brazil
Bibliographic record
Abstract
The notion of planetary urbanization has recently mobilized different strands in the field of urban studies and has generated extensive debates. This emerging research agenda aims to revise inherited concepts and produce a new vocabulary of urbanization through the construction of an ex-centric perspective that dislocates the focus of analysis from its conventional center: the city. The idea of extended urbanization is thus an imperative concept for it operationalizes this theoretical decentering and permits the exploration of urban questions beyond city-centrism, while encompassing urban agglomerations. This article discusses the conception of extended urbanization. We examine its vital insertion into the contemporary agenda of planetary urbanization and present its original formulation in and from Brazil, developed by Roberto Monte-Mór in the 1980s. In order to foster a productive dialogue between these formulations, we discuss the contradictions embedded within the process of extended urbanization and highlight Monte-Mór’s main theoretical and empirical contributions – particularly regarding urban politics and extended citizenship in the Brazilian Amazon. We contend that extended urbanization as formulated in and from Brazil offers important developments and goes far beyond a mere interesting empirical case in and from Brazil. Instead, it illuminates contemporary questions regarding planetary urbanization.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".