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Record W2905759142 · doi:10.32920/ryerson.14657238.v1

Encouraging farm gate sales and marketing through land use policy

2021· preprint· en· W2905759142 on OpenAlexaffabout
Heather Britten

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsToronto Metropolitan UniversityAlgonquin College
FundersDe La Salle University
KeywordsSustainabilityScale (ratio)Consumption (sociology)BusinessMarketingLand useAgricultural economicsEconomicsGeographyEngineeringSociologyCivil engineering

Abstract

fetched live from OpenAlex

This research brings together concepts of sustainability, a local food system, and farm gate marketing. With these concepts, the research explores two scales of policy planning with regards to land use in Ontario, and answers the question: In Southern Ontario, what is the impact of land use on a farmer's ability to sell at the farm gate? Despite the seemingly simple and small-scale nature of a farmer selling his/her produce at their own farm gate, there is surprising complexity to the myriad policies that apply. The dynamic relationship between eaters, farmer, and planners presents particularly interesting challenges for planners in Southern Ontario. Understand [sic] the local food system and engaging in local food consumption begins to address larger issues of sustainability and farm viability. By providing farmers with opportunities, through land use planning policy, they are able to engage with eaters at the farm gate and accomplish place-making activities.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.133
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.001

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 head0.024
GPT teacher head0.228
Teacher spread0.204 · 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 designTheoretical or conceptual
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
Published2021
Admission routes2
Has abstractyes

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