Sustainable Development of Oil Sands and Host Communities: Preliminary System Dynamics Assessment
Bibliographic record
Abstract
The global endowment of heavy oil and bitumen is vast, with recent estimates of approximately 2787.3 billion barrels (bbl) for deposits in place and 333.03 bbl for deposits recoverable. This resources are found in about 70 nations with the largest deposits located in Canada (Alberta), and Venezuela, others deposits in Africa include Nigeria, Angola, and The Democratic Republic of Congo.In Nigeria, the process for development of the Oil Sands (OS) resources located within a belt covering about 120 by 4-6 square kilometer area and extending through four states is gradually advancing with various policy formulation and decision-making activities ongoing. To attain a sustainable development of the OS, policy makers (decision makers) need to take into full consideration the local environmental governance perspective as represented by the host communities in which this resource is located. Since they will be the immediate recipients of the effects of development, appropriate policies that incorporate host community perspective need to be formulated. In this study, a logical framework for implementing sustainable OS and community was developed to capture interactions between local community, policy formulation process and OS development, from the three dimensions of sustainable development. This framework was then applied through System Dynamics methodology to identify cause-and-effect relationships and to project the trends of the identified indicators. A system dynamics model christened ‘POM-SOS-LC’ was attempted to capture interactions between local community, policy formulation process and OS development by modeling OS development and policy formulation process where host community involvement is a major determinant. Future work on this validated model shall comprise simulation over a 50 years period from 2015 considering the four policy scenario options identified in this paper.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".