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Record W2993570430 · doi:10.3138/jcs.2018-0018

Narratives and the New Farmer in Cape Breton: <i>“It’s Who We Are”</i>

2019· article· en· W2993570430 on OpenAlexvenueaboutno aff
Elizabeth Beaton

Bibliographic record

VenueJournal of Canadian Studies · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureContext (archaeology)SubsidySustainabilityNarrativeEconomic growthOptimismEconomicsSociologyBusinessMarketingGeographyMarket economy

Abstract

fetched live from OpenAlex

Can small, diversified farms thrive, or even survive, in Canada’s current agricultural milieu? Can they stand against the highly industrialized operations encouraged by Canadian policy, international trade, and capital interests? This study suggests that there is reason for optimism. Well-known visionaries, Canadian and worldwide, note a “new trajectory” in the context of the looming failure of current systems in agriculture, based on concerns for the environment and on the relationship between producers and consumers. Approaches to small, diversified farming operations come under several headings: economical, post-productive, civic. But it is the concrete experiences of individuals, families, and communities that truly give weight to the potential for sustainable food production. This research on Cape Breton Island farming—where self-sufficiency in food production is a strong tradition—presents a range of farming “styles” (as defined by Jan Douwe van der Ploeg) that are related to land acquisition, innovative marketing, support services, decisions about farm size and products, and the benefits of non-farm work as a farm subsidy. Interview narratives give voice to the actions of Cape Breton Island farmers who work within an “isolation paradox” as a way forward for their small farms.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.451
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.226
Teacher spread0.205 · 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
Published2019
Admission routes2
Has abstractyes

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