The up and down sides of oil and gas development in the Wood Buffalo Region of Alberta, Canada: Positioning Ghana for progressive gains
Bibliographic record
Abstract
Socio-economic benefits and challenges are consequential to oil and gas development, and effective planning is key to advancing favourable development outcomes. Drawing from Canada’s experience, this paper indicates that employment opportunities directly related to construction and operation of oil and gas production plants and indirect spin-off jobs needed to support the sector (e.g., manufacturing, transportation, accommodation and food, education and recreation) can create an enormous economic stimulus and revenue. However, the oil and gas production process and its associated population influx within and to surrounding communities of production sites to provide labour, goods and other services, come with challenges to develop and maintain the essential infrastructure and services. Also, increased demand by the industry for both skilled and unskilled labour, coupled with high earnings typical of this sector have resulted in high cost of living, parents spending less time with children at home due to intense work schedules, early employment and low school rates arising from easy access to unskilled jobs. Excessive alcohol and illicit drug uses, family breakdowns, violence and crime are as well evident. Unfortunately, a reactionary approach to handling these issues has frequently characterized the sector. Hence, we argue that for oil and gas production in Ghana to enhance its development would require royalty and tax regimes that are responsive to market conditions for reasonable revenue and yet support small producers. It also calls for collaborated efforts of all levels of government, industry, community and other connected stakeholders, in which all parties commit to proactive efforts in taking advantage of opportunities and addressing emerging issues to foster thriving communities and nation-wide economic development. Key words: Ghana, oil and gas development, socio-economic, benefits, challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".