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Record W4406167317 · doi:10.5539/jsd.v18n1p77

Agricultural Innovation as a Determinant of a Sustainable Transition in Rural Territories: Evidence from Costa Rica

2025· article· en· W4406167317 on OpenAlexvenueno aff
Jorge A. Rodriguez-Soto

Bibliographic record

VenueJournal of Sustainable Development · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsnot available
FundersUniversidad de Costa RicaUniversidad Nacional de Costa Rica
KeywordsAgricultureSustainabilityBusinessTourismDemocratizationCorporate governanceRural tourismNatural resource economicsEconomic growthGeographyEconomicsPolitical scienceEcologyTourism geography

Abstract

fetched live from OpenAlex

The predominant economic activities in rural territories are agriculture, tourism, and ecosystem services, with agriculture being the predominant one. The agricultural sector faces several critics regarding its sustainability and structural difficulties for innovation. Yet, some innovations in line with bioeconomy and the use of digital technologies in agriculture have potential to reconcile economic and environmental goals. Therefore, this research aims to determine the contribution of innovations in the agricultural sector to the sustainable transformation of rural territories in Costa Rica. This is achieved using a case study methodology, covering cases representative of more than 95% of the agriculture production of the country. Finding that these trends of innovation can foster the sustainability of the sector and rural territories while improving economic outcomes; that the biggest impact of these innovations is when the two trends, digital technologies and bioeconomy, are combined; and that the forms of governance play a paramount role in the democratization of innovation, especially for medium and small-sized producers, and in their diffusion within the territory.

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)0.001
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 score0.321
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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
Published2025
Admission routes1
Has abstractyes

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