MétaCan
Menu
Back to cohort

AWARENESS OF THE RESPONDENTS TOWARDS ACTIVITIES OF CO-OPERATIVE SOCIETY IN PATNA DISTRICT OF BIHAR

2021· article· en· W3189793636 on OpenAlexaff
Amit Kumar, Dipak Kumar Bose, Jahanara Jahanara, Saloni Sarraf

Bibliographic record

VenueInternational Journal of Advances in Agricultural Science and Technology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsAgricultureNon-invasive ventilationScheduleModernization theoryInvestment (military)BusinessProduct (mathematics)Work (physics)Economic growthSocioeconomicsAgricultural economicsEconomicsPolitical scienceGeographyEngineeringManagementMathematicsLaw

Abstract

fetched live from OpenAlex

Agriculture being the backbone of Indian economy also acts as the core of Bihar’s economy, employing 77 per cent of the work force and generating 35 per cent of the state domestic product. Meanwhile, the modernization and improvement of agriculture needs considerable investment. Whereas, Indian agriculture remained as poor man’s occupation institutional credit plays an important role in agricultural development. Thus, Agricultural Credit Societies (PACS) provide cheaper credit to agriculture. Descriptive research design is adopted. 120 respondents from Baruna, Chipra and Faziabad in Sampatchak block of Patna district in Bihar was purposively selected for the study since it had more number of co-operative society are present as compared to others. The primary data were collected with the help of interview schedule and the responses were recorded, classified and tabulated and appropriate statistical tools were employed. The results indicated that 48.33 per cent of the respondents were aware of the functions of the co-operative society and 45 per cent of the respondents opined that the co-operative society performance was average in marketing of agricultural products. It also implied that rules, regulations and laws should be enacted and standardized for better regulation of co-operative societies.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.300
Teacher spread0.288 · 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 designBench or experimental
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

Explore more

Same venueInternational Journal of Advances in Agricultural Science and TechnologySame topicAgricultural Economics and PracticesFrench-language works237,207