A Bibliometric Review of Career Education from Contemporary Literature with Vosviewer and Biblioshiny
Bibliographic record
Abstract
In recent years, career education has been an important topic for research due to its impact on student's personal development, especially on their decision-making and future employability. The need for present education systems to revamp themselves to suit the needs at different levels from adult education, higher education, schools, and various fields of research. However, there is much to be described in the body of knowledge from the perspective of bibliometric analysis. This article examines the term ‘career education from the Web of Science (WoS) database between the years 2018 and 2021. The bibliometric analysis results have indicated the trends of career education as data is scooped up from the Web of Science database. In terms of selective content analysis on highly cited articles, this section will discuss the trends of career education in higher education, in schools, in adult education, and the impact of the Covid-19 pandemic. There will also be some discussions on limitations and challenges in career education and some future suggestions for research.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".