Who is Who? Identifying the Different Sub-groups of Secondary Stakeholders within a Community: A Case Study of the Niger Delta Region of Nigeria Communities
Bibliographic record
Abstract
The Nigerian oil and gas industry (NOGI) has over time been dominated by the Nigerian government and oil producing companies (OPCs). The influences of the community stakeholder on OPCs in the last three decades have been expressed in diverse ways by different community sub-groups through their concerns and interests, some of which have severely impacted on the NOGI. Community within this context is categorised as a primary stakeholder, while the sub-groups are secondary stakeholders that emerge from within the community. Hence, the success of the NOGI largely depends on the Nigerian government and the oil producing companies, and other players such as Non-Governmental Organisations recognising the community as a key player and having appropriate knowledge of the different sub-groups of secondary stakeholders within the community in order to understand their intentions, behaviour, interests, influences and interrelations. Such knowledge is relevant to the NOGI’s formulation of future oil and gas strategy.This study commences with an overview of the primary stakeholders (i.e. the Nigerian government, OPC and the community), their respective activities, participation and the link between these stakeholders with a specific focus on the NOGI context. Subsequently, various sub-groups of secondary stakeholders within the community and their respective interest(s) are identified in detail.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".