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Record W2994413971

An Importance-Performance Analysis Of the Motivations Behind Agritourism and Other Farm Enterprise Developments in Canada

2010· article· en· W2994413971 on OpenAlexvenueaboutno aff
Carla Barbieri

Bibliographic record

VenueJournal of rural and community development · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)BusinessAgricultureMarketingTourismContext (archaeology)Quality (philosophy)Economic growthEconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

Challenging conditions in the current agricultural context have encouraged farmers develop agritourism and other enterprises on their farmland. Previous research suggests that a complex set of personal and economic goals drive the creation and maintenance of agritourism and other on-farm diversification ventures. However, the extent which those goals are accomplished has not been verified. This study employs an importance-performance analysis (IPA) examine the level of accomplishment of different goals driving agritourism and on-farm entrepreneurial development in Canada. IPA shows that goals with high levels of both importance and accomplishment are to continue farming, to enhance personal/family quality of life, to increase or diversify the market, and to respond a market need or opportunity. Further, results show differences in goals between agritourism and other types of farm entrepreneurs. Study findings suggest that extension agents can focus on the operator goals considered be most important and yield higher levels of accomplishment as they promote agritourism and other farm enterprises. These results have important implications for rural well-being, as agritourism is suggested keep family farms economically feasible and revitalize local communities. Keywords: agritourism, farm enterprise diversification, rural tourism, importance-performance analysis, rural well-being, goal

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)0.001
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 score0.433
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.277
Teacher spread0.260 · 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

Citations68
Published2010
Admission routes2
Has abstractyes

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Same venueJournal of rural and community developmentSame topicDiverse Aspects of Tourism ResearchFrench-language works237,207