A Shift in Mindset for the Oil and Gas Industry by Ina Tan
Bibliographic record
Abstract
The oil and gas industry has always been a foundational aspect of any economy. For a long time, it has been thought that an economy simply cannot thrive and survive without an oil & gas backbone. Longwell (2002) firmly believes that the industrial revolution has pushed the complete necessity of this industry. Not a single country has thrived without a resource as necessary as the fossil fuel.The study begins by going into detail about the definition of the industry. It looks into a typical full scale oil & gas company with an upstream, midstream and downstream business. It defines these sectors and goes into detail about this industry’s impact to economy and the environment. The author then delves into an analysis of the Canadian market and how significant the industry’s impact is to the economy. This section gives us an idea into the role of the industry in this specific market. A further analysis on the industry is performed using a Porter’s Five Forces analysis and ending it with key takeaways on the industry as a whole.Key players of the industry in the Canadian market are then introduced and further analyzed using a SWOT analysis. Here the sense of similarity and overarching themes across the key players are realized. Interestingly, most literature gathered may actually be true to life in these key players.Prior to concluding, we look at the current general direction of the industry. Here, it was fascinating to see that more of the “old-tech” habits of the industry are emphasized. A lot of valuable takeaways here from a variety of authors concluding similar thoughts, the industry has not been able to see much value in investment in research and innovation. It then focuses on a particular innovation that may eventually be difficult to ignore – renewable energy and the slow but eventual rise of electric vehicles.Drawing on over a decade of industry experience, the author found this study particularly engaging, offering insights that align closely with real-world experiences. The study also proved to be enlightening and pushed the author to reimagine an alternate future. This study is aimed to raise questions and provoke, not only for people within the industry, but the general public.
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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) | 0.002 | 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 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".