MétaCan
Menu
Back to cohort
Record W3016979126

Transitions in Rural Areas Motivated by Demands For Ecosystem Services: Empirical Results From Portugal

2020· article· en· W3016979126 on OpenAlexvenueno aff
Rute Martins, Maria Rosàrio Partidärio

Bibliographic record

VenueJournal of rural and community development · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystem servicesAttractivenessSocial capitalRural areaContext (archaeology)Empirical evidenceGeographyEmpirical researchEconomic growthBusinessEconomic geographyEcosystemEconomicsPolitical scienceSociologyEcologyPsychologySocial science
DOInot available

Abstract

fetched live from OpenAlex

A transition in rural areas affected by in-migration is underway, with socialecological systems (SES) becoming more diverse in its activities and in its social capital. In this context, ecosystem services (ES) may be acting as a pull driver for incomers, while its delivery also depends on the increase of social capital in rural areas. This paper investigates this mutualism between ES and SES, particularly whether SES and the inherent ES can stimulate attractiveness to rural areas and promote development, while also benefiting from incoming skilled in-migration. The engagement of in-migrants with rural SES and their demand for ES in rural areas is examined through literature review, observing the phenomena in different cases worldwide, and illustrated with empirical observation in rural Portugal. Results from our empirical observation reveal that the arrival of in-migrants triggers the delivery of new ES such as provisional or cultural services and that these are key to generate positive outcomes for rural SES. Keywords: rural in-migrants, ecosystem services (ES), social-ecological systems (SES), agents of change, transition

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.318

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 science0.0000.000
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 head0.028
GPT teacher head0.236
Teacher spread0.208 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of rural and community developmentSame topicRural development and sustainabilityFrench-language works237,207