ADOPSI DAN IMPLEMENTASI KECAKAPAN LITERASI INFORMASI DAN LITERASI DIGITAL UNTUK AKSELERASI UMKM DI INDONESIA PASCA PANDEMI COVID-19
Bibliographic record
Abstract
Almost all countries in the world are facing the Covid-19 pandemic, which is spreading so massively. Affecting various aspects of life and causing a domino effect in many sectors including the economy. Indonesia's economy worsened in the second quarter (Q2) 2020, negative 5.32 percent and only started to recover in the second quarter of 2021, positive 7.07% thanks to investment and increased household consumption. The MSME sector plays a significant and strategic role in supporting and is predicted to be the engine of economic recovery after the Covid-19 pandemic. The positive contribution of MSMEs to GDP before the pandemic could reach almost 60%. However, now in a condition full of uncertainty, it is necessary to strengthen various ideas, innovations and collaboration of various parties so that MSMEs can get out of pressure and support economic growth. This study will look at the potential for the adoption and implementation of digital literacy skills and information literacy by involving librarians (library institutions), academics, and literacy activists supporting MSMEs in contributing to economic recovery after Covid-19. This study uses a qualitative-descriptive approach in order to present a detailed description of the setting and literacy skills for MSME actors. This research is general in nature (generalization) with the scope of its location in Indonesia, without mentioning specific areas. Primary sources of research are obtained from literature studies, studies of various media and journal articles which are assumed to be able to capture the general condition of society. The results show that the idea of adopting and implementing digital literacy skills and information literacy for MSME actors has an urgency to be implemented immediately as a solution to the challenges of possible innovation (behavior) changes in the 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".