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Record W2999433515 · doi:10.21138/bage.2813

From megaprojects to tourism gentrification? The case of Santa Cruz Verde 2030 (Canary Islands, Spain)

2019· article· en· W2999433515 on OpenAlexaboutno aff
Marcus Hübscher

Bibliographic record

VenueBoletín de la Asociación de Geógrafos Españoles · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
FundersFriedrich Naumann Stiftung
KeywordsMegaprojectGeographyTourismQuarter (Canadian coin)UrbanismGentrificationSustainabilityNeighbourhood (mathematics)Spillover effectUrban agglomerationEconomyEconomic geographyEnvironmental planningArchaeologyCivil engineeringArchitectureEngineeringEconomics

Abstract

fetched live from OpenAlex

The inner-city oil refinery in Santa Cruz de Tenerife, Spain, has been shaping the city’s urbanism as an employer, but also as a polluter and a physical barrier for more than 80 years. The megaproject Santa Cruz Verde 2030 aims at transforming this area into a mixed-use urban quarter. Based on a mixed methods approach, this paper analyses the impacts of the megaproject by means of document, spatial and statistical analyses. The project is estimated to increase the city’s green areas by 39 % and the number of hotel beds by 70 %, provoking a strong touristifaction. Santa Cruz Verde 2030 stands for a new type of megaprojects, offering a variety of uses and sustainability wordings. Nevertheless, the impacts might reconfigure the city’s urbanism as a whole, shifting centralities to its southwest. On a neighbourhood level, spillover effects are expected to have diverging consequences. While in the Los Llanos neighbourhood gentrification and tourism are fostered, in Buenos Aires the megaproject implies the opportunity to integrate this currently segregated quarter into the city. Against this background, the paper outlines the necessity of transparent planning and monitoring processes, in order to ensure the sustainability of this new urban quarter.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.290
Teacher spread0.281 · 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 designNot applicable
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

Citations9
Published2019
Admission routes1
Has abstractyes

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