Work readiness and trending avenues for future studies: a systematic literature review and bibliometric analysis
Bibliographic record
Abstract
Purpose This study aims to undertake a theoretical and technical exploration of the literature on Work Readiness (WR) through the implementation of a systematic literature review and bibliometric analysis. The present study addresses seven distinct research questions: (1) an examination of the descriptive features characterizing the literature on WR, (2) an analysis of trends in annual scientific publications related to WR, (3) the identification of the most pertinent and high-impact sources contributing to WR, (4) the delineation of the globally cited articles exerting the most influence on WR, (5) the determination of the most relevant countries associated with WR, (6) an evaluation of the outcomes derived from Bradford’s Law of Scattering and Lotka’s Law of scientific productivity in the context of WR, and (7) the identification of the prevailing research avenues that hold significance for future studies on WR. Design/methodology/approach The present study employed Systematic Literature Review (SLR) and bibliometric analysis mapping techniques to analyze 521 articles extracted from the Scopus database. The analysis utilized Biblioshiny software and VOSviewer software as the primary tools. Findings The findings reveal that WR constitutes a steadily expanding subject discipline, showcasing a notable 9.12% annual growth in scientific production spanning from 1975 to 2023. Australia, the USA, and Canada emerged as the most productive countries within the field of WR, as evidenced by their cumulative scientific production. The thematic map of keyword analysis suggests several burgeoning pathways for future researchers in the WR domain, including workplace learning, functional capacity evaluation, graduate WR, digital literacy, blended learning, resilience, and curriculum. Originality/value This study contributes to the WR discourse by providing a comprehensive literature review. The findings of this study hold significance for graduates, universities, employers, the higher education industry, and the broader community.
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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 | 0.081 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.163 | 0.119 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".