Investigation of Studies on Micro-credentials by Bibliometric Analysis Method
Bibliographic record
Abstract
Purpose: The aim of the study is to reveal the trend of micro-credential studies conducted at the higher education level and to present a general framework on micro-credentials. Design/Methodology/Approach: Bibliometric analysis method was used in the study. Web of Science (WOS) database, which is a widely used and reliable data source in literature searches, was preferred to search the articles. The search criteria were limited to articles with title, abstract and keywords. For this purpose, the Web of Science database was searched with the keywords 'micro-credentials' and 'micro credential'. 85 papers are identified and included in the analysis. Findings: The keyword analysis revealed that micro-credentials have strong links with higher education, digital badges, employment and lifelong learning. At the same time, the most cited countries and researchers were mostly from countries such as the USA, Australia and Canada. The co-occurrence analysis of keywords unveils five clusters representing trends in micro-competencies and employability, learning and skills, distance learning, digitalization and technology, quality assurance and sustainability. Highlights: In conclusion, micro-credentials are becoming increasingly important as a flexible and innovative learning model that responds to the needs of modern education systems and labor markets.
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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 | Observational | low |
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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.797 | 0.945 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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.
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".