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Record W2522404227 · doi:10.5565/rev/dag.376

Newcomers to farming: towards a new rurality in Europe

2016· article· en· W2522404227 on OpenAlexaff
Neus Monllor i Rico, Anthony M. Fuller

Bibliographic record

VenueDocuments d Anàlisi Geogràfica · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRuralityAgricultureGeographyEconomic geographyEnvironmental planningAgroforestryPolitical scienceRural areaEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

One of the oldest laments in agrarian development has been over the ageing and loss of the farm population but, for the first time since the Second World War, a noticeable return to farming is now taking place across most of Europe. These farm entrants we classify as Continuers and Newcomers. Research shows that they have different characteristics. Newcomers are characterized by their profiles (female and higher education), the barriers they face (access to land, capital and markets) and by the business models that they adopt (pluriactive and multifunctional). This paper describes the main features of Newcomers as they form a new and dynamic group in European rural society and contribute strong social motivations and practices to farming. This change may be referred to as a shift from an agroindustrial to an agrosocial paradigm and, together with new social and environmental relations in food systems, forms a new rurality in Europe.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.251
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations46
Published2016
Admission routes1
Has abstractyes

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