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Record W4318817281 · doi:10.56093/ijas.v87i6.71023

Entrepreneurial behaviour and socio economic analysis of mushroom growers in Karnataka

2017· article· en· W4318817281 on OpenAlexfundno aff
Mahantesh Shirur, N S SHIVALINGEGOWDA, M. J. Chandregowda, Rajesh K. Rana

Bibliographic record

VenueThe Indian Journal of Agricultural Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture Market Analysis Ukraine
Canadian institutionsnot available
FundersMultiple Sclerosis Scientific Research Foundation
KeywordsMushroomEntrepreneurshipPopulationNeed for achievementRegression analysisMarketingBusinessPsychologyBiologySociologySocial psychologyMathematicsStatisticsFood scienceDemography

Abstract

fetched live from OpenAlex

Mushroom demand is showing tremendous growth worldwide due to its nutritional and medicinal qualities.In spite of huge potential and need to meet protein demand of large vegetarian population, India's progress in mushroom entrepreneurship is not that impressive. Entrepreneurial behaviour along with other personal and socio-psychological traits of entrepreneurs plays a prominent role in achieving success in any enterprise. In the present study conducted among the mushroom growing entrepreneurs across Karnataka State, an effort was made to understand influence of identified variables on farmers' entrepreneurial behaviour. Five attributes of farmers and their units, six attributes of socio-psychological traits and four extension variables were studied for investigating their effect on entrepreneurial behaviour of the respondents. Regression analysis showed that, academic qualification, cosmopolitanism, self-reliance, mass media participation, extension participation and training were significantly contributing to the entrepreneurial behaviour of the respondents.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.231
Teacher spread0.220 · 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".

Quick stats

Citations13
Published2017
Admission routes1
Has abstractyes

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