Social Sustainability of Raw Rubber Production: A Supply Chain Analysis under Sri Lankan Scenario
Bibliographic record
Abstract
Raw rubber production is the sole foundation for the rubber product industry, rendering raw rubber to manufacture essential commodities to mankind, such as tires, condoms, surgical gloves, and so forth. Raw rubber production involves various stakeholders; however, literature focusing on the social impacts of the supply chains of raw rubber production has hereto been absent. Social life cycle assessment, a popular tool to assess the social impacts of a product or process and was deployed herein to assess the social profiles of three Sri Lankan raw rubber supply chains (crepe rubber, concentrated latex, and ribbed smoked sheets) in a cradle-to-gate manner. The Social Hotspots Database v4 on Sima Pro v9.3 was used for the analysis. Results indicated that Governance, Labour rights & decent work had been affected due to Corruption and Freedom of association & collective bargaining issues, mainly in Belarus and China. Proposed improvement options to address these touchpoints were found to be effective. If the importation of K-fertilizer shifted into countries with lower risks, such as Canada, Israel, and Lithuania, overall social risks associated with Corruption and Freedom of association & collective bargaining could be reduced by ca. 36% and 25%, respectively. As a result, social risks in the impact described above categories, i.e., Governance and Labor rights & decent work, were reduced by ca. 35–41% and ca. 17–20%, respectively. Managers may pay thorough attention to the hotspots identified herein in the first place and try to avoid them as much as possible. They may consider importation from the aforesaid low-risk countries while weighing the trade-offs with economic and environmental aspects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".