Bibliometric analysis of Indonesia's labor dynamics: Future works, digital transformations, and contemporary employment landscape shifts
Bibliographic record
Abstract
This study conducts a comprehensive literature review to understand the direction and trends in contemporary labor studies, emphasizing significant global issues attracting scientific attention. Employing a scientometric approach, recent research data is explored using bibliometric analysis. The research adopts a mixed-methods approach, utilizing National Labor Statistics and conducting Focus Group Discussions (FGD) for nuanced insights into labor conditions in Indonesia. A bibliometric analysis of Scientific Labor Research Articles in Scopus (2020-2022) identifies trends and classifies global labor-related topics. Results highlight challenges in the labor landscape, driven by technological advancements and globalization, impacting job security, creating skill gaps, and raising concerns about the Fourth Industrial Revolution. The informal sector, particularly pronounced in Indonesia, poses challenges related to poverty, inequality, and the gig economy. Emerging issues like informal care for the elderly, social capital, and informal learning call for nuanced policy approaches. Indonesia's aging population adds complexity, requiring sustainable support mechanisms for healthcare and social services. The digital landscape, specifically Fintech, plays a significant role, yet research gaps persist. Bridging the digital talent gap is crucial for effective digital transformation, necessitating collaboration between government, educational institutions, and industry players. Challenges in Fintech development highlight the importance of initiatives promoting digital literacy, ethical practices, and regulatory frameworks. In conclusion, a holistic and collaborative approach is essential for navigating complexities and fostering sustainable economic growth.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.028 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".