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Record W4406229844 · doi:10.33795/abdimas.v11i2.6489

ONLINE AND OFFLINE MARKETING MENTORING AND TRAINING FOR SMEs IN JOYOGRAND MERJOSARI MALANG

2024· article· en· W4406229844 on OpenAlexaff
Umi Khabibah Umi, Pudji Herijanto, Fullchis Nurtjahjani, Nurdjizah Nurdjizah, Lilies Nur Aini

Bibliographic record

VenueJurnal Pengabdian Kepada Masyarakat · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsRegional Municipality of Niagara
Fundersnot available
KeywordsPromotion (chess)BusinessProduct (mathematics)Social mediaMarketingSales promotionDatabase transactionAdvertisingMeaning (existential)Online and offlineTraining (meteorology)DisseminationSales managementEngineeringPsychologyWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Assistance and training for SMEs in RW 8 Joyogrand Merjosari Malang Housing is a collaboration with the world of education. This is because there is a need for knowledge about marketing, both online through social media and offline through tools in the form of product catalogs. The catalog displays all the products produced, along with their prices, so this will really help potential consumers in recognizing and knowing what products are produced. The final thing that is hoped for is that a buying and selling transaction will occur between them. If you understand the meaning of promotion, you will know the true purpose of promotion. Not just communication to attract interest or introductions. The aim of promotion is to generate sales from capturing the market, as well as increasing the number of sales, increasing awareness of the sustainability of the buying and selling relationship. The training program for presenting promotional ideas on Facebook/Instagram and the Product Catalog is one of the training programs that can help disseminate product information. Likewise for UKMs in RW 08. They need to open themselves to other people regarding what they produce. By being known by other people, it will increase sales

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.042
GPT teacher head0.315
Teacher spread0.273 · 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
Published2024
Admission routes1
Has abstractyes

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