Social Lifecycle Impact Assessment of Informal Petroleum Products Retailing in Nigeria
Bibliographic record
Abstract
Our economic activities have both the desired and undesired environmental and socioeconomic consequences. The severity of impacts varies with stakeholders. Social lifecycle assessment helps us to evaluate how a product or process affect the workers, the consumers, the community, the value chain and the society. Unlicensed retailers buy petroleum products such as petrol, kerosene and diesel in plastic jerry cans when they hear the news of impending strike by petroleum workers or impending price hike. These unlicensed retailers often hoard the products in their homes with the aim of selling them later to the consumer’s exorbitant prices. This study utilizing the 2009 UNEP/SETAC’s social lifecycle assessment (sLCA) guidelines and the associated sLCA methodological sheets evaluated the socioeconomic impacts of illegal petroleum products retailing on the retailers, the consumers and the community. Preliminary results showed that it serves as an alternative employment opportunity. Due to high tendency for fire hazards, it poses health and safety risks to retailers, consumers and the community alike. This study is expected to serve as an eye opener to policy makers, law enforcement agencies and the general public on the need for preventive care against the attendant consequences of informal petroleum products retailing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".