A systematic literature review on the role played by sustainability matters in the educational environment
Bibliographic record
Abstract
Given the recent developments in sustainability reporting frameworks, this paper aims to systematically review the research on sustainability matters concerning its implementation in the educational area within the accounting-related courses or higher education institutions overall from a more comprehensive scope of analysis. For this purpose, a systematic literature review combined with a bibliometric analysis was applied, using the Scopus database to collect the papers published in this century (since 2000 onwards). The final sample is comprised of 56 papers, covering different clusters of analysis. The findings indicate an increase in sustainability publications, which aligns with the growing relevance of such issues worldwide. In recent years, studies with practical applications and a more diverse set of methodologies have increased instead of the initial focus on theoretical essays and analyses. Nevertheless, there are still some gaps that can serve as avenues for future research, regarding, for instance, the papers’ geographical scope, thematic area and methodologies proposed. This study expands to further topics not found in the literature such as what the classical social science theories have been proposed for evaluation, and the main results/conclusions the studies have reached. Finally, it suggests avenues for future research from the identified gaps and then contributes to the literature.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".