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Record W2399424222 · doi:10.12653/jecd.2015.22.1.0069

A Study on the Importance-Performance Analysis of Farm Party Participants

2015· article· en· W2399424222 on OpenAlexaboutno aff
Seon‐Hee Kim

Bibliographic record

VenueJournal of Agricultural Extension & Community Development · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicKorean Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)BusinessPromotion (chess)ScheduleVariety (cybernetics)Inclusion (mineral)Agricultural scienceMarketingOperations managementEngineeringGeographyPsychologyComputer scienceManagementPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This study conducted IPA of each item related to the farm party operation aiming at the participants to farm parties. The survey was carried out on the participants to the farm parties held in Damyang County from October 11th to November 22nd, 2014, and 103 questionnaires were used for the final analysis. The specific results of the IPA are as follows. In the first quarter of 'maintenance and management performance' are included the total of seven items. They are a harmonious view with the surrounding environment, friendliness of the farmer and the party staff, posting the event schedule and providing the program, proper disposition of the staff in the farm, an exchange among farm party participants, food that makes use of farm produce and the convenience of gaining information. In the second quarter of 'intensive improvement' are included eight items. They are cleanliness of the facilities such as restroom and basin, providing the pamphlets or promotion materials of the farms in the farm party, things to experience utilizing the farm produce grown in the host farm, various events, smooth progress of the farm party program, inclusion of the indigenous native food, variety of things to buy including farm produce and information delivery about the farm produce on sale.

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)0.002
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 score0.019
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.130
GPT teacher head0.290
Teacher spread0.160 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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