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Record W4237454337 · doi:10.32920/ryerson.14661483.v1

Imagining the farm: spectacle, nationalism, and agri-tourism in Canada

2021· preprint· en· W4237454337 on OpenAlexaboutno aff
Karen Poetker

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsSpectacleTourismNationalismModernityAgriculturePolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

"My decision to explore the spectaclization of rural and farm life in Canada was fuelled by the desire to answer the following questions: What motivates the nostalgia and the longing that people have for farming, pioneer and rural life? Why are people longing for this? What is it about modernity that is so disrupting and fragmenting that people would pay money to visit an old farm, to milk a cow, to pick some apples? How are farm tourism and nationalism connected? Is the farm as tourist site a physical manifestation of the desire to locate a strong national identity? What are the implications and complications of this transformation of the farm? The nature and length of this research paper is insufficient in dealing with the topic of farm tourism in all its detail. Rather than offer a conclusive discussion on the nature and implications of farm tourism, I hope this paper will bring to light issues of local and rural manifestations of nationalism, otherness, longing and fragmentation as well as call attention to the implications and complications that potentially arise out of agri-tourism" -- From Introduction, page 4.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.896

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0400.020
Scholarly communication0.0120.003
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.000

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 head0.011
GPT teacher head0.195
Teacher spread0.184 · 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 designNot applicable
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 routes1
Has abstractyes

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