Investigation of workplace literacy in Indonesia to enhance employability opportunities
Bibliographic record
Abstract
Indonesia's enduring vision 2045 requires sustained growth driven by productivity and human resources quality. A goal attainable only through a workforce proficient in critical thinking and digital literacy. Despite the acknowledged significance of digital literacy, there is limited empirical evidence on its effects, especially concerning employment prospects. While information and digital literacy are increasingly recognized as vital competencies within organizations, existing literature has somewhat overlooked the literacy levels of employees. This study aims to explore the impact of information and digital literacy on employees' perspectives regarding the utility and user-friendliness of digital technologies, subsequently assessing the implications for their overall employability in Indonesia. A survey involving 258 Indonesians. Data collecting is conducted through questionnaires and analysis using structural equation modeling investigates the factors influencing employability using the Technology Acceptance Model. The findings reveal that in Indonesia, there is a heightened emphasis on computer literacy and information literacy in both educational and professional realms. This might be due to the Indonesian education system and industry standards prioritizing these specific digital competencies, considering them essential for navigating technology, accessing information, and effectively utilizing digital tools in the professional sphere.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".