STRATEGI SOSIAL MEDIA, KECAKAPAN LITERASI INFORMASI DAN LITERASI DIGITAL START-UP SIGER INNOVATION HUB (@SIGERHUB) SEBAGAI INKUBATOR BISNIS UMKM DI PROVINSI LAMPUNG
Bibliographic record
Abstract
The Covid-19 pandemic has had a tremendous impact on the economy, from its emergence until now after this pandemic began to subside. In 2021 almost the whole world faces an economic downturn and causes a very deep contraction as almost all countries carry out strict mobility restrictions. In Indonesia itself, the economy has deteriorated starting in the second quarter (Q2) 2020. Then in the following periods it can start to rise, starting from the second quarter of 2021, which turned positive at 7.07% of the impact of investment and an increase in household consumption. It turns out that the MSME sector has a very significant and strategic role to support these improvements and even in the future it is predicted that it will continue to move as an engine of economic recovery after the Covid-19 pandemic. It should also be noted that the positive contribution brought by MSMEs will be able to save the economy from conditions of lack of certainty. In order to strengthen this, support and various ideas, innovation and collaboration from various parties are needed so that MSMEs can get out of pressure and support economic growth. Therefore, this study wishes to contribute by looking at the potential for adopting and implementing social media strategies and digital literacy skills and information literacy implemented in various Siger Innovation start-up programs and activities in supporting MSMEs in contributing to economic recovery after the Covid-19 pandemic. 19, especially for Lampung Province. This study uses a qualitative-descriptive approach in order to present a detailed description of the setting and literacy skills for MSME actors. The results of the study show that the social media strategy and the adoption of ideas and the implementation of digital literacy and information literacy skills for prospective and MSME actors in Lampung Province have urgency to be implemented immediately as a solution to the challenges of possible innovation (behavior) changes (behavior) habits and operations of the MSME sector after Covid-19 . Accelerate the transformation of MSMEs through strengthening the digital literacy and information literacy sectors to support MSME business functions (production, promotion and marketing) after Covid-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".