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Record W2742353443

From Leisure to Necessity: Urban Allotments in Alicante Province, Spain, in Times of Crisis

2017· article· en· W2742353443 on OpenAlexaffvenue
Ana Espinosa Seguí, Barbara Maćkiewicz, Marit Rosol

Bibliographic record

VenueACME: An International Journal for Critical Geographies · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAllotmentSpeculationPovertyRecessionUrbanizationAgricultureUnemploymentUrban agricultureGeographyEconomic growthBusinessEconomics
DOInot available

Abstract

fetched live from OpenAlex

Based on a comprehensive study of allotment gardens in the province of Alicante, this article enhances research on urban agriculture in two ways. Firstly, we explain the specific histories of urban allotments in Spain, that differ from the well-rehearsed stories of North America and also Northern Europe. Secondly, we show that a focus on urban allotments can provide a better understanding of changes in the economy, in land-use and in urban-rural relations in times of crisis. After two decades of Spain’s “urbanization tsunami”, in the mid 2000s a new way of combining urban life with agricultural functions emerged: through allotments, municipalities intended to promote environmentally-oriented leisure activities, enhance urban green landscapes and revive traditional vegetable gardens ( huertas) . At first, these projects catered mostly to pensioners, including foreigners coming from countries with long traditions of urban allotments. As the economic recession intensified in 2009, allotments had to re-define their goals in a social environment now defined by high unemployment and impoverishment. Today, most of the projects target people at risk of poverty and social exclusion and their primary functions are productive, therapeutic and educational. We also show that the global economic crisis of 2008 in a way contributed to the revaluation of agricultural land use, although the spectre of land-speculation is still very present.

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)0.001
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 score0.026
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.306
Teacher spread0.289 · 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

Citations31
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueACME: An International Journal for Critical GeographiesSame topicUrban Agriculture and SustainabilityFrench-language works237,207