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Record W3185075636 · doi:10.37838/unicen/est.29-071

La estructura productiva en la Región Metropolitana de Buenos Aires. De la valorización financiera (1976-2001) a las transformaciones en el siglo XXI

2021· article· es· W3185075636 on OpenAlexaboutno aff
Eduardo M. Basualdo, Leandro Marcelo Bona, Pablo Manzanelli

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2021
Typearticle
Languagees
FieldSocial Sciences
TopicRegional Development and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaGeographyQuarter (Canadian coin)IndustrialisationPopulationEconomyWelfare economicsCapital (architecture)Political scienceEconomicsDemographySociology

Abstract

fetched live from OpenAlex

La Región Metropolitana de Buenos Aires (RMBA) está constituida por una serie de distritos que rodean a la Ciudad Autónoma de Buenos Aires de Argentina. En ella se concentra el epicentro productivo y poblacional, pues alberga aproximadamente un tercio del producto bruto total y un cuarto de los habitantes del país. Este trabajo se propone analizar cómo respondió la estructura económica de la Región Metropolitana de Buenos Aires a los distintos patrones de acumulación de capital en la Argentina reciente. Para ello se investigan los cambios socioterritoriales en conexión con las transformaciones en la economía política, con particular énfasis en la industria y el empleo en el periodo 2003-2019, y luego se traza una delimitación cardinal-espacial (sur, oeste, norte) que identifica los núcleos estructurales de cada región. Las evidencian indican que los cambios en el patrón de acumulación registrados entre 2003 y 2015 permitieron detener parcialmente el proceso de desindustrialización, aunque no así el de estratificación social y urbana observados durante valorización financiera (1976-2001) en la Región Metropolitana de Buenos Aires, en tanto ambas tendencias regresaron y se potenciaron desde 2016.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.018
GPT teacher head0.285
Teacher spread0.268 · 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.

Study designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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Same venueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Same topicRegional Development and InnovationFrench-language works237,207