MétaCan
Menu
Back to cohort
Record W3081945713

Socio-psycho-economic profile of the mobile phone using farmers of Mirzapur district of Uttar Pradesh

2018· article· en· W3081945713 on OpenAlexaboutno aff
Amit Singh, Arun Kumar Singh

Bibliographic record

VenueJournal of Pharmacognosy and Phytochemistry · 2018
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsnot available
Fundersnot available
KeywordsUttar pradeshCasteAgricultureSocioeconomicsMobile phoneNon-invasive ventilationGeographyQuarter (Canadian coin)Socioeconomic statusToxicologyDemographyEngineeringSociologyBiologyPolitical sciencePopulationTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

The present study attempts to view the socio-psycho and economic profile of mobile phone using farmers of Mirzapur District of Uttar Pradesh of India. The research was undertaken purposively in Mirzapur district of Uttar Pradesh as KVK Barkachha under the IAS- BHU is actively using mobile phone technology for dissemination of information. In Mirzapur district there are 8 community development blocks. Among those, two blocks were selected i.e. Madihon block and Rajgarh Block for the present study. The study showed more than half (53.50 %) of the respondents belongs to middle age group and 22.50 per cent of the respondents had high school level of education. 45.50 per cent of the respondents belonged to OBC caste. 52.50 per cent of the respondents had joint family. 64.50 per cent of the respondents had marginal landholding. 81.00 per cent of the respondents had farming as their occupation. 69.50 per cent of the respondents had income level between 62000 to 334000 rupees. 49.00 per cent of the respondents were member of at least one organisation. 78.50 per cent of the respondents had medium level of economic motivation. 63.50 per cent of the respondents had medium achievement motivation. 60.00 per cent of the respondents had medium value orientation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.301
Teacher spread0.279 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pharmacognosy and PhytochemistrySame topicICT in Developing CommunitiesFrench-language works237,207