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Record W4388449142 · doi:10.1007/978-3-031-15233-7_12

Young Farmers’ Access to Land: Gendered Pathways into and Out of Farming in Nigara and Langkap (West Manggarai, Indonesia)

2023· book-chapter· en· W4388449142 on OpenAlexfundno aff
Charina Chazali, Aprilia Ambarwati, Roy Huijsmans, Ben White

Bibliographic record

VenueRethinking rural · 2023
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
FundersInternational Fund for Agricultural DevelopmentChina Agricultural UniversityInternational Institute of Social Studies, Erasmus University RotterdamCanada Research Chairs
KeywordsLivelihoodAgrarian societyGeographyAgricultureContext (archaeology)SocioeconomicsEconomic growthSociologyArchaeologyEconomics

Abstract

fetched live from OpenAlex

Abstract This chapter describes rural young men and women’s pathways out of and (back) into farming in two villages in West Manggarai district, Flores island, Eastern Indonesia. The chapter has seven sections. First, we describe the methodology and our sample in the two sites. The second section then provides the geographical and social context and describes livelihood patterns in the research villages. Next, we present illustrative cases of young people’s pathways out of and (back) into farming, both young men and women, followed by an account of the tensions arising through the process of land transfer between generations in the fourth section. The fifth and sixth sections focus on young people’s farming practices and how the government supports young farmers. In the final section, we reflect on how gender, generation, and class combine to shape the pathways of young farmers out of and (back) into farming and their access to agrarian resources.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.050
GPT teacher head0.256
Teacher spread0.207 · 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 designQualitative
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

Citations6
Published2023
Admission routes1
Has abstractyes

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