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Record W3167861527 · doi:10.1111/cuag.12269

Agricultural Persistence and Potentials on the Edge of Northern Ontario

2021· article· en· W3167861527 on OpenAlexfundaboutno aff
Elizabeth Finnis

Bibliographic record

VenueCulture Agriculture Food and Environment · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAgricultureVisionResilience (materials science)Futures contractPersistence (discontinuity)Psychological resilienceAgricultural productivityNatural resource economicsFlexibility (engineering)GeographyEnvironmental resource managementEnvironmental planningBusinessEconomicsSociologyPsychologyEngineeringManagement

Abstract

fetched live from OpenAlex

Abstract Drawing on farmers’ lived experiences, I explore factors that shape agricultural persistence in the Parry Sound District, Ontario, Canada. Local farming is embedded in broader contexts and is place‐based and specific. Agricultural persistence and resilience are shaped in part through individual factors, such as flexibility in response to change, the valuing of local agricultural heritage, and the determination to farm. However, attention to specific agricultural needs is critically necessary to help ensure agricultural futures in the district. I demonstrate the ways that attention to place‐based experiences pinpoints the need for localized understandings and supports to ensure agricultural viability and contribute to diverse and valued visions of agriculture and food production within the province.

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)0.001
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 score0.037
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.005
Scholarly communication0.0030.001
Open science0.0010.002
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 head0.012
GPT teacher head0.154
Teacher spread0.141 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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