Four Decades Research on Higher Vocational Education: A Bibliometric Review
Bibliographic record
Abstract
Vocational education is seen as an integral part of the educational system, either at the school level or at a post-secondary level. It is indicated that vocational education has immensely influenced life and plays a vital role in evolving societies worldwide. Thus, analysing these scientific domains evolution and creation are essential. This study discusses the bibliometric review of scholarly research performed in higher vocational education in recent years. The study uses the Scopus database and a wide variety of bibliometric metrics, including publications, citations, and publishing patterns. A graphical visualisation of bibliographic details using the VOS viewer software is also presented. Results show that the number of studies covering this subject has fluctuated, with the highest number of publications being 65 in 2020. Often, by reviewing publications and research areas, it can be construed that higher vocational education research is diverse, including social sciences, computer science, and engineering. The bibliometric review conducted offers a rigorous and thorough view of higher vocational education research that may benefit higher vocational education practitioners and researchers keen on the pursuit of enhancing or discovering new knowledge in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.068 | 0.089 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".