Bibliographic record
Abstract
Purpose – Misconception of issues surrounding green supply chain management (GSCM), as well as a paucity of relevant information on the tangible benefits of GSCM practices in organizations was justification for this literature review. The paper aims to discuss this issue. Design/methodology/approach – The study has been conducted by analyzing and critiquing secondary data obtained from numerous sources of similar subject. The research topic has been examined in detail. Findings – The outcomes provide an overview of what GSCM practices entail, strategies successful companies have used to incorporate GSCM practices within their organizations and its impact on the industry. Research limitations/implications – The research conducted in this study is limited to one country, i.e. Canada, and as such further research should be carried out by incorporating a larger array of participants so as to obtain a more generalized conclusion. Practical implications – The study contributes to an understanding of the importance of GSCM practices on not only the economic success of a business, but the positive effects on the environment. The results will help in the reduction in emissions of carbon dioxide and other green house gases, thus impacting on climate change. Originality/value – Despite increasing awareness, the implementation of GSCM techniques continue to be deterred by lack of government initiatives and commitment of companies involved in the supply chain. Unless it is given precedence, the benefits of GSCM will continue to elude us. This study provides an opportunity to study a model which has met with critical success, rejuvenate it and consequently mandate its adoption in efforts to attain sustainability.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".