Bibliographic record
Abstract
The primary goal of this study was to reveal and emphasize the fundamental discoveries and perspectives present in current trends to fostering employee engagement (EE) in sustainability at the workplace. Scopus database was accessed by using the “Green” OR “Fostering Employee” AND “Sustainability” keywords over the period 2014 to 2024, 124 out of 227 articles were selected by using the PRISMA methodology. VOSviewer and Excel software for data analysis. Findings revealed that sustainability, EE, and leadership are central terms linked to HRM, environmental sustainability, social sustainability, and job satisfaction. Publication trends steadily increase, peaking at 124 articles in 2024, indicating growing research activity. Sustainability (Switzerland) journal publishes the most articles but ABAC Journal has the highest impact with 329 citations. France and India are leading research on sustainability and EE; however, Spain, India, and France are at the forefront of recent research, with initial contributions originating from the US and expanding to the UK, China, and Canada.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".