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Record W7133576240

The City in a Quarter: An Urban Village with Many Names

2016· article· en· W7133576240 on OpenAlexaboutno aff
Fernando Monge Martínez

Bibliographic record

VenueUNED repository · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)Quarter (Canadian coin)MainstreamVariety (cybernetics)Global cityOld townUrban structure
DOInot available

Abstract

fetched live from OpenAlex

Malasaña is an old neighbourhood of Madrid, a quarter with character, though with many denominations. To most Madrilenians, as well as to the hipsters who live there and to tourists, the quarter is known as Malasaña. For older, retired residents of modest means it is Maravillas. Yet, its official, and least known, name is Universidad. Whatever its name, this neighbourhood is not just home to a large variety of small shops, old and new, traditional and hipster, to mainstream franchises on its fringes and small, specialized commercial spaces, old bars many without charm and innovative ones in old buildings; Malasaña is what Jane Jacobs (1961) would call an Urban Village. This article has two main goals: to show how, while maintaining some old charms, the urban village of Malasaña has been reconfigured by the new micro cultures of alternative groups, creative classes, hipsters and visiting suburbanites. It also intends to show how this bottom-up transformation connects with global trends found elsewhere. There are two major dynamic drives in this neighbourhood; one from within — the traditional, old quarter with a distinctive mix of population, the other from without — the transient (but key) inhabitants of the current service-oriented urban realm, mostly youth from other areas of the city and the suburbs and tourists. As a metropolis, Madrid is a good example of emergent practices related to the social, cultural and economic dimensions that reshape a vital, singular place of the old city. It is also a good case study in dealing with the global and local processes that shape the contemporary city.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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