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Record W4385605625 · doi:10.5539/sar.v12n2p34

The Influence of Place Attachment on Farmers’ Succession Plans: A Mixed Methods Study

2023· article· en· W4385605625 on OpenAlexvenueno aff
Mark E. Burbach, Stephanie M. Kennedy, Shari J. Kunert

Bibliographic record

VenueSustainable Agriculture Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEcological successionSuccession planningPlan (archaeology)Place attachmentExploratory researchIdentity (music)Family farmQualitative researchSociologyPsychologyGeographyPublic relationsSocial psychologySocial sciencePolitical scienceArchaeologyEcologyAgriculture

Abstract

fetched live from OpenAlex

A farm family’s land succession plan is vital to ensure that high-value farmland continues to benefit the family for generations to come. However, many farmers have been reluctant to develop succession plans. The purpose of this study was to determine the influence of place attachment on farmers’ land succession planning. This exploratory mixed methods research involved farmers within 10 years of retirement age (55 years of age or older), both with and without a land succession plan. Surveys and interviews utilized Raymond, Brown, and Weber’s (2010) five dimensions of place attachment: place identity, place dependence, nature bonding, family bonding, and friend bonding. Survey results showed farmers with a succession plan had significantly higher place identity, place dependence, nature bonding, and overall place attachment than farmers without a succession plan. Seven themes emerged from the qualitative analysis of interviews of farmers with a succession plan and six themes emerged from interviews of farmers without a succession plan. Three themes: connection to family, sense of community, and enjoyment of the outdoors were held in common. This study adds to the literature exploring the complex factors affecting the transition of the family farm to the next generation.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.038
GPT teacher head0.388
Teacher spread0.351 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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