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Record W1536636626

New to Farming: Exploring the motivations and experiences of small-scale farmers in Nova Scotia

2015· dissertation· en· W1536636626 on OpenAlexaboutno aff
Hope Perez

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNova (rocket)Scale (ratio)AgricultureGeographyAgricultural scienceForestryPolitical scienceAgricultural economicsEngineeringCartographyArchaeologyEconomicsEnvironmental scienceAeronautics
DOInot available

Abstract

fetched live from OpenAlex

The romanticism of rural landscapes was an essential aspect of the 1960s and 1970s back-to-the-land movement. Urbanites flocked to rural spaces as an escape from the plight of humanity in cities. In the contemporary back-to-the-land movement, perceptions of the rural utopian idyll influence the use of rural spaces. Farmers have the unique opportunity to express their conceptions of rural life through farming methods. This study explores the motivations, barriers and challenges of small-scale farming in Nova Scotia. Six neo-farmers, meaning those who do not come from family farming backgrounds, were interviewed about their experiences. Farmers belong to a community of small-scale, alternative producers that rely on markets and community-supported agriculture. There was a clear transition from understanding farming as a lifestyle choice versus farming as source of income. Farmers were predominately motivated by health and environmental concerns. This study also explores farmers’ attitudes toward farming as a political act.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.287
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.266
Teacher spread0.211 · 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

Citations0
Published2015
Admission routes1
Has abstractno

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