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Impacts of Agritourism on Social Capital during the COVID-19 pandemic in Rural Ontario

2022· article· en· W4408460180 on OpenAlexaffvenueabout
Elizabeth MacLaughlin Nakashima

Bibliographic record

VenueRural Review Ontario Rural Planning Development and Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicSocial capital2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GeographySocioeconomicsEconomic growthEconomicsSociologySocial scienceMedicineVirologyOutbreak

Abstract

fetched live from OpenAlex

The development of agritourism has been a successful method used to diversify income and provide more financial sustainability for small scale farms that was used during and prior to the COVID 19 pandemic. The purpose of this research is to examine the current state of agritourism in southwestern Ontario 2 years into the COVID-19 pandemic. The pandemic safety protocols, defined by the Ministry of Health and local public health units, called for social distancing protocols which meant social events could not be hosted inside. Hosting events outdoors became a mode for safe social gatherings. There have been many opportunities with the development of outdoor social spaces; the future of social gatherings is still very uncertain because we are still in the midst of the pandemic. The research aims to identify the linkages between social experiences with agritourism and the knowledge and appreciation for local agri-food systems throughout the pandemic.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.330
Teacher spread0.291 · 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".

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Citations0
Published2022
Admission routes3
Has abstractyes

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